Bibliographic record
Abstract
done in committeee, whether that work entails scrutiny of the executive and fostering of governmental accountability, the development and refinement of policy, the representation of individual and group interests to government, or the legitimization of the entire political system.”1 This paper will deal with the pre-budget consultations undertaken by the Standing Committee on Finance and Economic Affairs (SCFEA) of the Ontario Legislature every year. The questions that this paper seeks to ask are: a) how effective are the hearings that take place every year; b) how much input does the committee’s recommendations and reports have in the actual budget process; and c) do the committee’s hearings allow for the voice of the public to be heard in the budgetary process. The paper will begin by a description of SCFEA, its membership and history, as well as the role of the committees in the Ontario Legislature as a whole. From then it will evaluate the effectiveness of committee hearings based on interviews and evidence, as well as a description of the budgets that have been delivered since 2003. In the interests of both space and resources, I will confine my analysis to the current Liberal
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.277 | 0.078 |
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".