Unity in the eye of the beholder? Reasons for decision in theory and practice in the Ontario Works program
Bibliographic record
Abstract
This article interrogates reasons for decision, a central concept in Canadian public law scholarship. Using spatiotemporal scale as an analytical tool, it shows how unified reasons may be more easily recognized at the scale of judicially reviewable administrative decisions common to public law scholarship, yet elusive at the scale of front-line decision making. It then investigates how a variety of mechanisms, including data entries and notes, function together behind the front lines of social assistance agencies in the province of Ontario. Drawing on qualitative research into caseworkers’ decision-making practices, this article illustrates how the ‘reasons’ for a particular administrative decision may be multiplied and fractured across software programs, emails, and physical case files. Further, it demonstrates how notes are both more and less than reasons. As they perform three internal communicative tasks central to administrative governance – recording evidence, explaining decisions, and justifying potentially contentious outcomes to other administrative insiders – notes facilitate decision-making practices that ensure institutionally acceptable outcomes are reached, even as one note may not fully capture the logic underlying a particular decision. Ultimately, this article aims to motivate theoretically inclined legal scholars to reconsider the concept of reasons for decision in light of the decision-making practices of front-line administrators.
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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 | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.090 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".