Introduction: time, temporality and timescapes in administration and policy
Bibliographic record
Abstract
This article surveys time, temporality and timescapes in the study of public administration and public policy. While references to temporal categories, such as timing, sequence, speed, duration, time budgets, time limits or time horizons, are ubiquitous in political science, there are few systematic treatments of time in administration and policy-making. The special issue which this article introduces focuses on analyses that seek to explain policy development over time; the link between time and power; and the role that visualisation may play in helping to understand change over time. Taken together, the papers seek to advance the debate by 1. exploring different facets of time and how they affect government and public policy; 2. paying attention to time as an institution and a resource; 3. discussing the temporal features of politics and administration, such as, e.g., term limits, and of public policy-making, such as policy cycles or policy horizons; 4. exploring time from both diachronic-historical and synchronic perspectives; 5. debating the status of time in different theoretical traditions in political and policy analysis; and 6. examining time from a methodological standpoint.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".