Autobiographical reflections on bridging the policy–academy divide
Bibliographic record
Abstract
In this roundtable, a panel of distinguished scholars reflects on the possibilities, dangers and rewards of academics transgressing the policy–academy divide. Both Krasner and Nye point to the differences in culture between policy and academic realms. Krasner shows how the policymakers are primarily concerned with conceptual framing while academics are more concerned with testing propositions. He describes how the garbage-can model of policymaking accurately suggests that academic ideas may matter, but if they do happenstance and luck are more important than quality. Nye points to how policymaking is heavily influenced by the pressures of time. While he acknowledges the danger of academics compromising truth in the face of power, he notes that scholars can equally lose their objectivity. Stein warns that the powerful ‘evolutionary instinct’ of social scientists being able to make ‘better’ policy is a ‘conceit’ and scholars must ultimately be prepared to leave if they believe that the decision they opposed violates their moral principles and are operationally costly. Keohane concludes the panel by praising scholars who work effectively in policy, but also pointing out some of the risks of scholars venturing into this very different world. Quoting Keynes, he advises academics interested in policymaking to ‘beware the bad fairy’. The original discussion took place at the International Studies Association Annual Conference held in San Francisco in 2008. Below are the edited transcripts of the discussion, including further post-panel reflections
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.025 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".