Towards Sustainable Performance Measurement Frameworks for Applied Research in Canadian Community Colleges and Institutes
Bibliographic record
Abstract
Applied Research (AR) in Canadian community colleges is driven by a mandate, via the collective voice of Colleges and Institutes Canada – a national voluntary membership association of publicly supported colleges and related institutions – to address issues of interest to industry, government, and/or community. AR is supported through significant federal and provincial level funding mechanisms as well as funding from the private sector. Performance measurement tools have largely been developed by government agencies such as the Natural Sciences and Engineering Research Council (NSERC) and the National Research Council of Canada (NRC), which are external to the colleges that engage in AR. This paper presents an overview of AR in Canadian community colleges and institutes and provides recommendations for the development of sustainable performance measurement frameworks for AR in Canadian community colleges and institutes.
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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 | 0.203 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.028 | 0.038 |
| Science and technology studies | 0.023 | 0.036 |
| Scholarly communication | 0.038 | 0.012 |
| Open science | 0.014 | 0.020 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".