Tribunals and Guidelines: Exploring the Relationships between Fairness and Legitimacy in Administrative Decision-Making
Bibliographic record
Abstract
The objective of this paper is to address two questions: why do administrative tribunals such as the Immigration Refugee Board resort to developing guidelines, and what are the principles and values which legitimize these initiatives? The role of tribunals in policy-making and/or policy-implementing raises important questions. For example, to whom are tribunals accountable for the development and application of guidelines where the functions of a tribunal - especially the adjudicative functions - are intended to be independent of government?The authors seek to understand better the dynamics of tribunals’ role in the policy process. They propose a classification of guidelines based on the function they perform in administrative proceedings and provide an analysis of the normative framework underlying guidelines. The authors explore how a legal analysis of guidelines might shed on the theory and practice of public administration. The authors conclude that in the absence of a nuanced understanding of the legal status of guidelines, the relationship between administrative practice and the rule of law remains uncertain and unstable.
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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 | 0.087 | 0.273 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.012 | 0.074 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".