Holding out the promise of Lasswell's dream: Big data analytics in public policy research and teaching
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract While the emergence of big data raises concerns regarding governance and public policy, it also creates opportunities for diversifying the toolkit for analysis for the policy sciences as a whole, i.e., research concerning policy analysis as well as policy studies. Further, it opens avenues for practice, which together with research requires adaptation in teaching curricula if policy education were to remain relevant. However, it is not clear to what extent this opportunity is being realized in public policy research and teaching. In this study, we examine the prevalence of big data analytics in public policy research and pedagogy using bibliometric analysis and topic modeling for the former, and content analysis of course titles and descriptions for the latter. We find that despite significant scope for application of various big data techniques, the use of these analytic techniques in public policy has been largely limited to select institutions in a few countries. Further, data science has received limited attention in policy pedagogy, once again with significant geographic variation in its prevalence. We conclude that, to stay relevant, the policy sciences need to pay more attention to the integration of big data techniques in policy research, pedagogy, and thereby practice.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.134 | 0.161 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it