BRIEF TO THE STANDING COMMITTEE ON FINANCE AND ECONOMIC AFFAIRS Pre-Budget Consultations
Bibliographic record
Abstract
professors and academic librarians in Ontario’s universities, believes that the Government of Ontario must take immediate action to invest in Ontario universities to ensure continued access and quality. The principal challenges facing Ontario universities are as follows: The increased enrolment demand means that university spaces must be increased by one new McMaster University each year for the next four years. A wave of faculty retirements will require thousands of replacement hires. The student/faculty ratio in Ontario universities has increased by 25 % over the last decade and is a measure of the deteriorating quality of the education experienced by students. These challenges require that 15,000 new faculty be hired over the next decade. Neither the private sector nor tuition fee revenue is an appropriate or adequate response to this public policy challenge. What is needed is a new investment of $500 million into the base operating grants of Ontario universities over the next four years. OCUFA contends that this investment will meet articulated government goals to ensure
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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.036 | 0.015 |
| Insufficient payload (model declined to judge) | 0.117 | 0.043 |
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".