Unity in the Eye of the Beholder? Reasons for Decision in Theory and Practice
Bibliographic record
Abstract
This paper unravels the claim to unified reasons for decision underlying much public law scholarship. Using Valverde’s concept of spatiotemporal scale as an analytical tool, it shows how unified reasons only make sense at the scale of judicially-reviewable administrative decisions. In response, it investigates how data, notes, and reasons fluidly shift at the scale of front-line decision-making in Ontario’s welfare program. Drawing from a qualitative study of welfare caseworkers, this article suggests that reasons for administrative decisions are multiple and fractured throughout software programs, emails, and physical files. Further, it demonstrates how notes and data entries function together to articulate why caseworkers make particular decisions, and that caseworker notes perform three internal communicative tasks central to administrative governance: recording evidence, explaining decisions, and justifying potentially contentious actions.
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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 | 0.041 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.130 |
| Scholarly communication | 0.023 | 0.035 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".