Practicing Change, Changing Practice: Gallery Educators’ Professional Learning in Times of Reckoning and Upheaval
Bibliographic record
Abstract
Art museums are increasingly responding to calls for exhibitions, community engagement, and institutional changes that confront and unsettle taken-for-granted knowledge, structures, and ways of working. Grounded in such a dynamic and evolving field, this qualitative study asked the following: What does gallery educators’ own learning look like -- and what motivates it? How does ongoing competency building inform critical dialogue with visitors and support wider efforts to reshape the field through an ethos of social justice? Drawing on tenets of critical pragmatism, transformative adult learning, and constructivist grounded theory, my thesis comprised three manuscripts based on findings from two series of interviews with gallery educators in Canada and Scotland. This article highlights my findings, contextualizing my analyses on the shifting ground shaping gallery education in both countries. In doing so, it contributes to both a relative paucity of scholarly research on critical professional learning in art museums and an emerging body of literature addressing the impact of the coronavirus pandemic on the working lives of gallery educators and the futures that lie ahead.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.043 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".