Response: Matters of (im)mobility: beyond fast conceptual and methodological readings in policy sociology
Bibliographic record
Abstract
In this special issue response paper, we pick up from prior discussions to suggest areas where we think policy sociology can benefit from further conversation and research. In particular, we bring together our respective readings and work on policy mobilities, in conversation with the contributions of this special issue, to think further about what policy mobilities and related orientations can bring to the development of policy sociology. We specifically focus our attention on four areas for further conceptual and methodological extension in relation to policy mobilities and related approaches in policy sociology of education: (i) temporality, (ii) scale, (iii) land, and, finally, (iv) methodological diversity. In doing so, we advocate against ‘fast’ readings of literature from other fields, while emphasizing the value of transdisciplinary work in policy sociology and critical policy studies.
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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 | 0.017 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.044 | 0.053 |
| Insufficient payload (model declined to judge) | 0.029 | 0.012 |
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".