What Can a University Gallery Do?
Bibliographic record
Abstract
University art galleries can serve wide and varied purposes. Like traditional art galleries and art museums, the conventional university art gallery may take on the role of preserving and archiving particular histories by amassing curated collections of cultural value along with showcasing curated contemporary and period artworks and artists. While the conventional, well-funded gallery may be focused on the past, securing objects to remember it, unfunded galleries are pressed to gaze forward, to rethink their roles. This chapter describes how The LAIR Galleries, a collection of gallery sites at Lakehead University's Thunder Bay, Ontario campus, seeks to build flourishing communities and research capacity. To answer the focal question, What can a university gallery do?, the LAIR Galleries evolving vision, history, functions, and current considerations are described in this chapter.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.041 | 0.020 |
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".