Practicing change, changing practice: \nGallery educators’ professional learning in times of reckoning and upheaval
Bibliographic record
Abstract
Art museums are responding to increasing calls for exhibitions, community engagement, and institutional change that confront and unsettle taken-for-granted narratives, knowledge, policies, and practices. Grounded in my work as a mid-career gallery educator and trainer, this qualitative study asks what gallery educators’ learning looks like and what motivates it. How does it shape or respond to change? Through this line of inquiry, I sought to better understand how myriad paths to competency building can support or hinder critical gallery dialogue, an ethos of social justice, and wider efforts to make art museums more representative, responsive, and relevant to the publics they are meant to serve. \nThis manuscript-based thesis draws on tenets of critical pragmatism, transformative adult learning, and constructivist grounded theory to analyze individual and group interviews with gallery educators in Canada and Scotland. The first manuscript examines how volunteer guides identify, navigate, and reflect on challenging subject matter in both their ongoing learning and gallery dialogue with visitors. The second manuscript focuses on freelance gallery educators’ professional learning within the overlapping contexts of the coronavirus pandemic and protests for racial justice in 2020. The third and final manuscript considers the potential for gallery educators’ informal professional learning to inform internal policies and procedures. \nI link the three manuscripts with a prelude that highlights gallery educators’ learning as they describe it and two bridging texts that situate my findings in their wider contexts. These self-reflexive texts, which address decolonial turns and whiteness in art museums, draw on additional literature and my own professional learning trajectory over the duration of my doctoral studies. I conclude with final reflections on transformation as a theoretical starting point, learning through the writing process, and implications for future research. This thesis contributes to both a paucity of scholarly research on critical professional learning in art museums and an emerging body of literature addressing the impacts of a global pandemic on museum workers.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.034 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".