Keep@Downsview: an evolving shared print project
Bibliographic record
Abstract
Keep@Downsview is a shared print partnership between six Canadian academic institutions. This article describes the evolution of the partnership from what began as simply an operationally focused collaboration for shared preservation space, in order to release collections space in on-campus libraries, to a partnership that is involved in the larger shared print discussions in North America and which has refocused its strategic directions enabling a more intentional evolution of the partnership. Four strategic directions were identified prior to the outbreak of the pandemic. Since the onset of Covid-19 it has become apparent that several influencing factors related to the pandemic could present opportunities for reconsidering the identified strategic directions and how we may wish to implement them. To ensure we fully capitalize on the opportunities presented, members from all partner schools will engage in a visioning exercise that will inform development of our next iteration of the strategic directions.
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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 | 0.028 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.020 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".