Category Archives: Academic innovation or navel gazing

I know my audience, but does my other audience know I know my audience?

‘Know your audience’ is a key phrase for anyone trying to convey a message successfully. To ‘know your audience’ is to understand the rules they use to make sense of your message, and therefore the adjustments you have to make to produce an effective message. Simple examples include:

  • The sarcasm rules. The first rule is fairly explicit. If you want to insult someone’s shirt, you (a) say ‘nice shirt, pal’, but also (b) use facial expressions or unusual speech patterns to signal that you mean the opposite of what you are saying. Otherwise, you’ve inadvertently paid someone a compliment, which is just not on. The second rule is implicit. Sarcasm is sometimes OK – as a joke or as some nice passive aggression – and a direct insult (‘that shirt is shite, pal’) as a joke is harder to pull off.
  • The joke rule. If you say that you went to the doctor because a strawberry was growing out of your arse and the doctor gave you some cream for it, you’d expect your audience to know you were joking because it’s such a ridiculous scenario and there’s a pun. Still, there’s a chance that, if you say it quickly, with a straight face, your audience is not expecting a joke, and/ or your audience’s first language is not English, your audience will take you seriously, if only for a second. It’s hilarious if your audience goes along with you, and a bit awkward if your audience asks kindly about your welfare.
  • Keep it simple stupid. If someone says KISS, or some modern equivalent – ‘it’s the economy, stupid’, the rule is that, generally, they are not calling you stupid (even though the insertion of the comma, in modern phrases, makes it look like they are). They are referring to the value of a simple design or explanation that as many people as possible can understand. If your audience doesn’t know the phrase, they may think you’re calling them stupid, stupid.

These rules can be analysed from various perspectives: linguistics, focusing on how and why rules of language develop; and philosophy, to help articulate how and why rules matter in sense making.

There is also a key role for psychological insights, since – for example – a lot of these rules relate to the routine ways in which people engage emotionally with the ‘signals’ or information they receive.

Think of the simple example of twitter engagement, in which people with emotional attachments to one position over another (say, pro- or anti- Brexit), respond instantly to a message (say, pro- or anti- Brexit). While some really let themselves down when they reply with their own tweet, and others don’t say a word, neither audience is immune from that emotional engagement with information. So, to ‘know your audience’ is to anticipate and adapt to the ways in which they will inevitably engage ‘rationally’ and ‘irrationally’ with your message.

I say this partly because I’ve been messing around with some simple ‘heuristics’ built on insights from psychology, including Psychology Based Policy Studies: 5 heuristics to maximise the use of evidence in policymaking .

Two audiences in the study of ‘evidence based policymaking’

I also say it because I’ve started to notice a big unintended consequence of knowing my audience: my one audience doesn’t like the message I’m giving the other. It’s a bit like gossip: maybe you only get away with it if only one audience is listening. If they are both listening, one audience seems to appreciate some new insights, while the other wonders if I’ve ever read a political science book.

The problem here is that two audiences have different rules to understand the messages that I help send. Let’s call them ‘science’ and ‘political science’ (please humour me – you’ve come this far). Then, let’s make some heroic binary distinctions in the rules each audience would use to interpret similar issues in a very different way.

I could go on with these provocative distinctions, but you get the idea. A belief taken for granted in one field will be treated as controversial in another. In one day, you can go to one workshop and hear the story of objective evidence, post-truth politics, and irrational politicians with low political will to select evidence-based policies, then go to another workshop and hear the story of subjective knowledge claims.

Or, I can give the same presentation and get two very different reactions. If these are the expectations of each audience, they will interpret and respond to my messages in very different ways.

So, imagine I use some psychology insights to appeal to the ‘science’ audience. I know that,  to keep it on side and receptive to my ideas, I should begin by being sympathetic to its aims. So, my implicit story is along the lines of, ‘if you believe in the primacy of science and seek evidence-based policy, here is what you need to do: adapt to irrational policymaking and find out where the action is in a complex policymaking system’. Then, if I’m feeling energetic and provocative, I’ll slip in some discussion about knowledge claims by saying something like, ‘politicians (and, by the way, some other scholars) don’t share your views on the hierarchy of evidence’, or inviting my audience to reflect on how far they’d go to override the beliefs of other people (such as the local communities or service users most affected by the evidence-based policies that seem most effective).

The problem with this story is that key parts are implicit and, by appearing to go along with my audience, I provoke a reaction in another audience: don’t you know that many people have valid knowledge claims? Politics is about values and power, don’t you know?

So, that’s where I am right now. I feel like I ‘know my audience’ but I am struggling to explain to my original political science audience that I need to describe its insights in a very particular way to have any traction in my other science audience. ‘Know your audience’ can only take you so far unless your other audience knows that you are engaged in knowing your audience.

If you want to know more, see:

Kathryn Oliver and I have just published an article on the relationship between evidence and policy

How far should you go to secure academic ‘impact’ in policymaking? From ‘honest brokers’ to ‘research purists’ and Machiavellian manipulators

Why doesn’t evidence win the day in policy and policymaking?

The Science of Evidence-based Policymaking: How to Be Heard

When presenting evidence to policymakers, engage with the policy process that exists, not the process you wish existed



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Filed under Academic innovation or navel gazing, agenda setting, Evidence Based Policymaking (EBPM), Psychology Based Policy Studies, public policy, Storytelling

Five advantages of blogging

This is my third ‘hey, let’s blog’ event, so it finally dawned on me to write a blog post about it. See also Fiona Miller’s account of the Stirling event.

I don’t know much about blogging research, so will focus on my personal experience of its advantages. One frequent academic argument against blogging is that it takes you away from more important parts of the job, such as teaching and research. My argument is that it helps you do both things more effectively.

See also the accounts of the disadvantages, which often relate to the ways in which they make you vulnerable to personal abuse on social media (examples 1, 2, 3).

Advantage 1: Clarity

Writing a blog has improved my academic writing. When you blog, you write for a non-specialist audience. You use less jargon or explain its meaning and value. You assume that people will not read your work unless you front-load the ‘reveal’. You need a catchy and tweetable title, to provide a ‘hook’ in the first sentence, and to show your work in a few hundred words (perhaps to encourage people to read more of your work). When you develop these skills, you can use them while writing journal article titles, abstracts, and introductions.

If you like, you can also write a blog post instead of relying on the paper/ powerpoint combo for workshops and conferences, since a 4-paper panel at conferences is usually an endurance test, and a blog post reminds you to say why people should be interested in the paper (e.g. recent examples on evidence/ policy and Scottish independence).

Advantage 2: Timeliness

It can take years for people to read an article you publish in a top journal. Sometimes the article is worth the wait. In other cases, I think it’s best to see this work as part of a package in which the article is one of the last things to appear. There is a good case to be made for taking your time to get articles right, but a less good case to keep it a secret while you do so.

Advantage 3: Exposure

It’s now common to say that we make better links with practitioners and policymakers by making our writing more accessible (short, punchy, and one click away). In my experience, the biggest payoff has been with other academics. Politics colleagues will mention my blog (and textbook) more than my articles. I can also use introductory blog posts to communicate ideas with colleagues in other disciplines – and/ or in other countries – without expecting them to do weeks of homework on the foundational texts. In each case, it works partly because we struggle to find the time to read, and appreciate a short story. Indeed, my articles are one click away on my website, but very, very, very, very few people read them.

However, you don’t need a personal blog. In fact, my most exposureyish posts have been elsewhere, including two in the Guardian’s political science blog (on evidence-based policymaking, and (with Kathryn Oliver) the dilemmas that arise when we seek it), some on the LSE blog (I tried really hard to compare tobacco and alcohol policy – look! There’s a video!), and many in The Conversation.

Advantage 4: Teaching and Learning

Teaching. The most-used page of my website hosts a series of 1000 Word summaries of policy concepts (the ‘policy cycle’ got 26000 hits in 2016). I use them, like a gateway drug, to teach undergraduate and MPP modules: they can get a feel for the concept quickly then do further reading. They now come with podcasts, which I use instead of lectures (for workshops). Other academics also use the podcasts, particularly when their students are new to policy studies (e.g. David P. Carter).

Learning. I also ask my students to write blog posts as part of their coursework, to help them learn how to write in a concise and punchy way for a non-academic audience. In most cases, students excel at this kind of work, as part of a package of assessment in which they learn how to communicate the same insights in many different ways.

Advantage 5: Unexpected benefits

When I started blogging I didn’t really know what it was for. I used to copy and paste my article abstracts, or complain about David Cameron’s handling of Scottish independence. This was at a time in which colleagues at my former University were reticent about self-publicity, and sending round a link to a new journal article via the departmental email was pushing it a bit. Now, self-promotion seems to be part of the job, and we might expect some benefits without really knowing what they’ll be. For example, my links with some very interesting people in places like the European Commission and Alliance for Useful Evidence have arisen largely from blogging.

We all have different things that tickle us in life. For me, the most tickling part of the unexpected benefit of blogging is that I now (almost!) top the following google searches: policy cycle, multiple streams, advocacy coalition framework, punctuated equilibrium theory, the politics of evidence based policymaking, and the psychology of policymaking. I’m also doing my best to push out the other Paul Cairney from the first page of google, but Wikipedia is getting in the way. The more serious point is that a personal blog might need to generate attention through social media first, before it catches fire and rises up the search engine pages.

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Why the pollsters got it wrong

We have a new tradition in politics in which some people glory in the fact that the polls got it wrong. It might begin with ‘all these handsome experts with all their fancy laptops and they can’t even tell us exactly how an election will turn out’, and sometimes it ends with, ‘yet, I knew it all along’. I think that the people who say it most are the ones that are pleased with the result and want to stick it to the people who didn’t predict it: ‘if, like me, they’d looked up from their laptops and spoken to real people, they’d have seen what would happen’.

To my mind, it’s always surprising when so many polls seem to do so well. Think for a second about what ‘pollsters’ do: they know they can’t ask everyone how they will vote (and why), so they take a small sample and use it as a proxy for the real world. To make sure the sample isn’t biased by selection, they develop methods to generate respondents randomly. To try to make the most of their resources, and make sure that their knowledge is cumulative, they use what they think they know about the population to make sure that they get enough responses from a ‘representative’ sample of the population. In many cases, that knowledge comes from things like focus groups or one-to-one interviews to get richer (qualitative) information than we can achieve from asking everyone the same question, often super-quickly, in a larger survey.

This process involves all sorts of compromises and unintended consequences when we have a huge population but limited resources: we’d like to ask everyone in person, but it’s cheaper to (say) get a 4-figure response online or on the phone; and, if we need to do it quickly, our sample will be biased towards people willing to talk to us.* So, on top of a profound problem – the possibility of people not telling the truth in polls – we have a potentially less profound but more important problem: the people we need to talk to us aren’t talking to us. So, we get a misleading read because we’re asking an unrepresentative sample (although it is nothing like as unrepresentative as proxy polls from social media, the word ‘on the doorstep’, or asking your half-drunk mates how they’ll vote).

Sensible ‘pollsters’ deal with such problems by admitting that they might be a bit off: highlighting their estimated ‘margin of error’ from the size of their sample, then maybe crossing their fingers behind their backs if asked about the likelihood of more errors based on non-random sampling. So, ignore this possibility for error at your peril. Yet, people do ignore it despite the peril! Here are two reasons why.

  1. Being sensible is boring.

In a really tight-looking two-horse race, the margin of error alone might suggest that either horse might win. So, a sensible interpretation of a poll might be (say), ‘either Clinton or Trump will get the most votes’. Who wants to hear or talk about that?! You can’t fill a 24-hour news cycle and keep up shite Twitter conversations by saying ‘who knows?’ and then being quiet. Nor will anyone pay much attention to a quietly sensible ‘pollster’ or academic telling them about the importance of embracing uncertainty. You’re in the studio to tell us what will happen, pal. Otherwise, get lost.

  1. Recognising complexity and uncertainty is boring.

You can heroically/ stupidly break down the social scientific project into two competing ideas: (1) the world contains general and predictable patterns of behaviour that we can identify with the right tools; or (2) the world is too complex and unpredictable to produce general laws of behaviour, and maybe your best hope is to try to make sense of how other people try to make sense of it. Then, maybe (1) sounds quite exciting and comforting while (2) sounds like it is the mantra of a sandal-wearing beansprout-munching hippy academic. People seem to want a short, confidently stated, message that is easy to understand. You can stick your caveats.

Can we take life advice from this process?

These days I’m using almost every topic as a poorly-constructed segue into a discussion about the role of evidence in politics and policy. This time, the lesson is about using evidence correctly for the correct purpose. In our example, we can use polls effectively for their entertainment value. Or, campaigners can use them as the best-possible proxies during their campaigns: if their polls tell them they are lagging in one area, give it more attention; if they seem to have a big lead in another area; give it less attention. The evidence won’t be totally accurate, but it gives you enough to generate a simple campaigning strategy. Academics can also use the evidence before and after a campaign to talk about how it’s all going. Really, the only thing you don’t expect poll evidence to do is predict the result. For that, you need the Observers from Fringe.

The same goes for evidence in policymaking: people use rough and ready evidence because they need to act on what they think is going on. There will never be enough evidence to make the decision for you, or let you know exactly what will happen next. Instead, you combine good judgement with your values, sprinkle in some evidence, and off you go. It would be silly to expect a small sample of evidence – a snapshot of one part of the world – to tell you exactly what will happen in the much larger world. So, let’s not kid ourselves about the ability of science to tell us what’s what and what to do. It’s better, I think, to recognise life’s uncertainties and act accordingly. It’s better than blaming other people for not knowing what will happen next.


*I say ‘we’ and ‘us’ but I’ve never conducted a poll in my life. I interview elites in secret and promise them anonymity.

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A Stern review for everyone?

The Stern reviewwas commissioned by the government to carry out the review of the Research Excellence Framework (REF) to ensure future university research funding is allocated more efficiently, offers greater rewards for excellent research and reduces the administrative burden on institutions’. In this post, I explain why no single policy can solve these problems uniformly: they affect scholars of different seniority, and different disciplines, very differently. The punchline is at the end.

My initial impression of the Stern review is that it has gone to great lengths to address the unintended consequences of the previous Research Excellence Framework. One of its key aims now is to try to anticipate the potential unintended consequences of its reduction of other unintended consequences! This is remarkably common in policymaking, perhaps summed up by Aaron Wildavsky’s phrase ‘policy as its own cause’: we enter a never-ending process of causing ripple effects when trying to fix previous problems.

The example of non-portability (#sternreview portable)

Take the example of one of the biggest problems:

Problem: there was a large incentive for Universities to ‘game’ the REF towards the end of the cycle: paying for 20% of the time of big name academics, or appointing them at huge salaries, to gain access to their 4 best publications. A policy of rewarding research excellence became a policy to (a) reward big transfers, undermining the efforts of other Universities and reducing their incentive to invest for the long term, and (b) boost the salaries of senior scholars (many of whom were already on 6-figure salaries), often to ridiculous levels.

Solution: non-portability. The idea is that you can move but your former employer gets to use the texts that you published while in your last job. So, there may now be less incentive to buy up the big names in the run up to the next REF.

Unintended consequence: uncertainty for early career researchers (ECRs) or scholars without permanent (open-ended) contracts. Many ECRs have expressed the concern that their incentives may suddenly change, from generating a portfolio of up to 4 excellent publications to secure a permanent post, to perhaps holding back publications and promising their delivery when in post. This could be addressed by the present review (ECRs could be included in the REF but be under no obligation to submit any publications), or perhaps by exempting ECRs since the policy is aimed at senior scholars (but the exemption would also have unintended consequences!).

Interpreting the problem through the lens of precarious positions

We will now enter a phase of debate driven by uncertainty and anxiety about the end result, and a lot of the discussion will be emotionally charged because many people will have spent maybe 8 years in education (and several years in low paid posts after it) and not know what to do next. It is relatively easy for people like me to say that the proposed new system is better, and for senior scholars to look on Stern as a big improvement, because people like me will be rewarded in either system (I will leave it to you to decide what I mean by ‘people like me’). It is more difficult for ECRs who are genuinely uncertain about their prospects.

The punchline: how does this look through the eyes of scholars in different disciplines?

The disciplinary lens is the factor that can often be most important but least discussed, for two key reasons:

  1. The general differences. Scholars operate in different ways. The ‘STEM’ (science, technology, engineering and mathematics) subjects are often described in these terms: you have large teams headed by a senior scholar; there is a hierarchy; you all work on the same research question; you publish many short articles as a team (with senior authors listed at the very start and end of the list of authors); you are increasingly driven by key metrics, such as personal citations and the ‘impact factor’ of journals . The humanities is often described like this: you are a lone scholar; you work on your own research question; you publish single-author work, and the big status symbol is the research monograph (book); these journal or other metrics do not work as well in your discipline. I am verging on caricature here (many ‘STEM’ scholars will work alone, and some humanities or social science scholars will operate laboratory-style teams), but you get the idea.

These differences feed into other practices: only in some subjects can it make sense for a University to ‘poach’ a whole team or unit; only in some subjects do ECRs need to develop their own portfolio of work; in some subjects, a PhD student or ECR effectively works for a senior scholar, while in others the PhD student has a supervisor but can set their own research agenda; in some subjects, it is automatic to include the senior scholar in an article you wrote, in others it would be seen as exploitation of the work of a PhD student or ECR. In some subjects, a CV with your name on team publications is the norm, while in others it would look like you do not have your own ideas.

  1. The REF reinforces these differences. You often find the impression that research exercises and metrics are there for the STEM subjects (or ‘hard sciences’) and not the humanities or social sciences: a process or review for Universities does not take into account the differences in incentives and practices across the disciplines, and some disciplines might lose out.

So, if you follow this debate on twitter, I recommend that you look at the bios of each participant to check their level of seniority and discipline because the Stern review is for all but it will affect us all in very different ways.

See also: James Wilsdon ‘The road to REF 2021: why I welcome Lord Stern’s blueprint for research assessment



Filed under Academic innovation or navel gazing, UK politics and policy

Q. Should PhD students blog? A. Yes.

I wish I could go back and rewrite everything I have published, including my PhD. If I knew then what I know now: I would get to the point quicker and describe its importance to a far wider audience than my supervisor and a few dedicated journal readers. To do so, I would exhibit the skills you develop when you write frequently for an ‘intelligent lay’ audience.

These are the writing traits that I think you develop when just writing for academics:

  1. You assume a specialist audience, familiar with key terms. So, you use jargon as shorthand without explaining its meaning. The downside is that the jargon often doesn’t have a particularly clear meaning. When you blog, you assume a non-specialist audience. You use less jargon, or you explain its meaning and value.
  2. You treat the exercise as a detective novel with a big reveal: a nice, vague opening discussion (passive tense optional), a main body of text to build up the suspense, and finally the big twist at the end. Ta da! Wow, I didn’t see that coming. When you blog, you assume that people will not read your work unless you front-load the reveal. You have a catchy and tweetable title, you provide a hook in the first sentence, and you only have a few hundred words in which to show your work (and encourage people to read the longer report).
  3. Or, you describe your hypotheses in a way that suggests that even you don’t know what will happen. Wow – I confirmed that hypothesis! Who knew? When you blog, it seems more sensible to use the language of hypotheses (or an equivalent) more simply, to explain what factors are most important to your explanation.

You can develop this skill by using a personal blog to describe your research progress and the value of your findings. However, it is also worth blogging in at least two other venues:

  1. Somewhere like the LSE blog, or Democratic Audit, in which the editors will try to summarise your argument in a short opening statement. This is very handy for you: did they summarise the main argument? If so, good. If not, look again to see if you explained it well.
  2. Somewhere like The Conversation, in which the editors will try to mess around with the title (to encourage more traffic) and wording (to make it punchier and quotable). This is a good exercise in which you can think about how far you want to go. Are you confident enough in your research to make such stark statements? Or, do you want to obfuscate and fill the argument with caveats? If the latter, you can think about the extent to which your argument is clear and defendable (it may well be – sometimes caveats and humility can be good!).

I also encourage advanced undergraduates and taught postgraduates to produce a blog post (albeit unpublished) alongside an essay or policy paper, because it is difficult to be concise, and the exercise helps develop a good life skill. Even without the blog exercise, I’d still encourage dissertation students (at the start of their research) to write up their argument/ plan/ work in a half-page document, so that we can see if it adds up to a coherent argument. You can do the same thing with a blog post, with the added (potential) benefit of some feedback from outside sources.

See also: there are resource sites which go into far more aspects of the writing process, such as and

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The Art and Skill of Academic Translation: it’s harder when you move beyond English

I have been writing about the idea of ‘translation’ in terms of ‘knowledge transfer’ or ‘diffusion’, which often suggests that there is a linear process of knowledge production and dissemination: knowledge is held by one profession which has to find the right language to pass it on to another. This approach has often been reflected in the strategies of academic and government bodies. Yet, the process is two-way. Both groups offer knowledge and the potential to have a meaningful conversation that suits both parties. If so, ‘translation’ becomes a way for them to engage in a meaningful way, to produce a common language that they can both ‘own’ and use. Examples include: the need for scientists to speak with policymakers about how the policy process works; the need for ‘complexity’ theorists to understand the limits to policymaker action in Westminster systems; the separate languages of institutions which struggle to come together during public service integration (key to local partnership and ‘joined up government’); and, the difference in the language used by service providers and users. We might also worry about the language we use to maintain interdisciplinary discussion (such as when ‘first order change’ means something totally different in physics and politics).

It’s not the same thing, but translating into another language, such as when conversing in English and Japanese, reinforces the point in an immediately visible way. In both directions, English to Japanese, and vice versa, it is clear that the recipient only receives a version of the original statement – even when people use a highly skilled interpreter. Further, if the statement is quite technical, or designed to pass on knowledge, the gap between original intention and the relayed message is wider still.

This point can be made more strongly in a short lecture using interpretation. As academics, many of us have been to conferences in English, and witnessed a presenter trying to cram in too much information in 15 minutes. They give a long introduction for 10, then race through the slides without explaining them, simply say that they can’t explain what they hoped, or keep going until the chair insists they stop. You don’t really get a good sense of the key arguments.

In another language, you have to reduce your time to less than half, to speak slowly and account for translation (simultaneous translation is quicker, but you still have to speak very slowly). You have to minimise the jargon (and the idioms) to allow effective translation. Or, you need to find the time to explain each specialist word. For example, while I would often provide an 8000 word paper to accompany a lecture/ workshop, this one is 1500. There is no visible theory, although theory tends to underpin what you focus on and how you explain it. It took 40 minutes to present, largely because I left a lot of topics for Q&A. I still had a hard time explaining some things. I predicted some (such as the difference between ‘federalism’ and ‘federacy’, and the meaning of ‘poll tax’ and ‘bedroom tax’) but realised, late on, that I’d struggle to explain others (such as ‘fracking’, or the unconventional drilling used to access and extract shale gas).

This sort of exercise is fantastically useful, to force you to think about the essential points in an argument, keep it short without referring to shorthand jargon, and explain them without assuming much prior knowledge in the audience, in the knowledge that things will just mean different things to different audiences. It is a skill like any other, and it forces on you a sense of discipline (one might develop a comparable skill when explaining complex issues to pre-University students).

Indeed, I have now done it so much, alongside writing short blog posts, that I find it hard to go back from Tokyo to jargon city. Each time I read something dense (on, for example, ‘meta-governance’), I ask myself if I could explain it to an audience whose first language is not English. If not, I wonder how useful it is, or if it is ever translated outside of a very small group.

This is increasingly important in the field of policy theory, when we consider the use of theories, developed in English and applied to places such as the US and UK, and applied to countries around the globe (see Using Traditional Policy Theories and Concepts in Untraditional Ways). If you can’t explain them well, how can you work out if the same basic concepts are being used to explain things in different countries?

Further, we don’t know, until we listen to our audience, what they want to know and how they will understand what we say. Let me give you simple examples from my Hokkaido lecture. One panellist was a journalist from Okinawa. He used what I said to argue that we should learn from the Scots; to develop a national identity-based social movement, and to be like Adam Smith (persevering with a regional accent, and a specific view of the world, in the face of snobbishness and initial scepticism; note that I hadn’t mentioned Adam Smith). Another panelist, a journalist from Hokkaido, argued that the main lesson from Scotland is that you have to be tenacious; the Scots faced many obstacles to self-determination, but they persevered and saw the results, and still persevere despite the setback (for some) of the referendum result (I pointed out that ‘the 45%’ are not always described as tenacious!). Another contributor wondered why Thatcherism was so unpopular in Scotland when we can see that, for example, it couldn’t have saved Scottish manufacturing and was perhaps proved correct after not trying to do so. Others use the Scottish experience to highlight a similar sense of central government imposition or aloofness in Japan (from the perspective of the periphery).

In general, this problem of academic translation is difficult enough when you share a common language, but the need to translate, in two ways, brings it to the top of the agenda. In short, if we take the idea of translation seriously, it is not just about a technical process in which words are turned into a direct equivalent in another language and you expect the audience to be informed or do the work to become informed. It is about thinking again about what we think we know, and how much of that knowledge we can share with other people.

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Filed under Academic innovation or navel gazing, Japan, public policy, Scottish independence, Scottish politics

Reviews of My Books

A review of Understanding Public Policy and Global Tobacco Control in Public Administration: Painter review of 2 Cairney books 2013

A review of Global Tobacco Control in Governance: Kurzer review of GTC in Governance 2014

A review of Understanding Public Policy from an early career academic:

Two reviews of Understanding Public Policy in Political Studies Review:

Richards review in PSR


Kihiko review in PSR

(they are both here

From someone keeping it succinct and numeric:

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