Equity practitioners' Canadian study tour: effective initiatives, learnings and reflections
Bibliographic record
Abstract
In June 2017, Louise Pollard, 2017 NCSEHE Equity Fellow, and Associate Fellows, Melinda Mann and Nicole Crawford, visited Canadian universities and participated in the Canadian Association of College and University Student Services (CACUSS) annual conference. The trio met with Student Experience staff from a range of institutions across the country. They discussed effective student success strategies; reflected on different approaches to supporting regional and remote students; and considered innovative ways to engage students in most need. During this presentation, the trios learnings and reflections will be shared. They will showcase effective initiatives from Canadian universities and explore how the initiatives, their learnings and reflections can shape the way universities in New Zealand and Australia engage and support regional and remote students now and in the future.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.048 | 0.014 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".