Draft Only: Comments Welcome Evaluating Regulatory Instrument Choice
Bibliographic record
Abstract
The task looked disarmingly simple. I was to review the recent literature on regulatory instrument choice. I would apply insights from this literature to the example of broadcasting regulation in Canada, not because broadcasting was the object of interest, but because, over the past forty years, just about every regulatory instrument imaginable had been used within this sector. Once the most appropriate conceptual framework had been chosen from the literature and this example, I would be in a position to attend to the real task to be done. It was to develop a protocol for the evaluation of regulatory instrument choice for three Canadian sectors that are newly coming under scrutiny and will require new policies as a result of Kyoto. A single independent tribunal regulates broadcasting, which is somewhat unusual in the Canadian context. The question of having a single overarching regulatory tribunal in the three targeted sectors was not on the table. Despite this obvious difference between broadcasting and these three targeted sectors, I thought it should be clear, once the first task was completed, which regulatory instruments held the most promise. The real task would follow in time: testing the results in the sectors of cement, oil sands and public and
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".