Bibliographic record
Abstract
Abstract Guidelines are a type of “soft law” that play an important role in contemporary public administration. Despite the propagation of guidelines in recent decades, their legal effects are often difficult to classify. Clearly, guidelines are neither legislation nor delegated or subordinate legislation, but they are nonetheless designed to influence people's behaviour. Distinguishing binding from non‐binding guidelines is an important issue because the permissible scope of their use often depends on bindingness. Yet there is no analytical framework available to determine bindingness. To fill this gap in the literature, I develop an analytical framework consisting of three indicia which help to distinguish binding guidelines from non‐binding guidelines: the presence or absence of imperative language, the level of detail and precision and the extent of effects on third parties. With the help of numerous examples drawn from the Canadian legal system, I explain how to distinguish binding from non‐binding guidelines, bringing analytical clarity to an important area of contemporary public administration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.016 | 0.064 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".