Value-for-Money in Saskatchewan K–12Educational Expenditures
Bibliographic record
Abstract
The recent release by the Organisation for Economic Co-operation and Development (“OECD”) of the results of the Programme for International Student Assessment (“PISA”) has served once more to focus the attention of Canadians on the thorny issue of standardised educational testing. We rightly felt a sense of pride in the accomplishments of the fifteen-yearold Canadians who, in the tests for literacy in reading, mathematics and science, ranked second in reading among the thirty-two countries tested and fifth in mathematics and science. Indeed, the fifteen-year-olds from Alberta actually topped the highest ranked country, Finland, in reading literacy, while those from British Columbia and Quebec were not far behind. In literacy in mathematics, the students from Quebec were second only to those of Japan, while those from Alberta and British Columbia followed closely. With the exception only of New Brunswick, which fell below the OECD average for scientific literacy, all of the Canadian provinces exceeded the 32-nation average in each of the literacy tests. The results for the Saskatchewan students tested were above the OECD averages, but trailed the Canadian average in each area tested. It is of interest to enquire what should be made of such results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".