The Moral Foundations of Public Engagement: Does Political Science, as a Discipline, Have an Ethics?
Bibliographic record
Abstract
In recent years, the discipline of political science has been the focus of extensive criticism from observers based both within and beyond the academy. This is reflected in a sizable number of scholars who have called for the discipline to recognize its obligations to the public, and especially to supporting active citizenship, promoting democratic participation and addressing major social challenges. This emphasis on ‘making political science matter’ has also been stressed beyond the academy as funders, politicians and potential research-users place ever-greater emphasis on incentivizing and rewarding ‘impact’, ‘relevance’ and demonstrable ‘public value’. The central argument of this article is that what has been missing from this debate is any sense of clarity around whether what is being demanded is greater engagement by political science as a discipline or greater engagement by political scientists as individuals. This raises distinctive questions about the moral foundations and professional ethics of political science which we explore not through a traditional focus on defending or sustaining liberal democracy but through a deeper and more subtle emphasis on the praxis of ‘doing’ political science.
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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 | 0.044 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.070 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".