Tuition Policy Instruments in Canada Public Policy Choices for What Problems
Bibliographic record
Abstract
AbstractUsing policy instruments as the unit of analysis and employing an instrument typology, this study considers policymaker goals and the effects of different policy actors and their influence strategies on selection. This study builds upon conceptions of choice approaches to policy instruments, using an analytical lens to describe policy actor perceptions and policy instrument use in three Canadian provinces to shed new light on the nature of higher education policy design.RésuméEn utilisant les instruments de politique comme unité d'analyse et en employant une typologie d'instruments, cette étude considère les objectifs des décideurs politiques et les effets des différents acteurs politiques et leurs stratégies d'influence sur la sélection. S’appuyant sur les conceptions des approches de choix des instruments de politique et utilisant une lentille analytique pour décrire les perceptions des acteurs politiques ainsi que les instruments de politique dans trois provinces canadiennes, cette étude servira de tremplin afin de jeter un nouveau regard sur la nature de la conception des politiques d'enseignement supérieur.Key Words: policy instruments; higher education; provincial policy; tuition policyMots-clés : instruments de politique; enseignement supérieur; politique provinciale; politique de scolarité
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 | 0.029 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".