Adult Education, Exhibitory Strategies and Museums
Bibliographic record
Abstract
This special edition of the Canadian Journal for the Study of Adult Education takes us into the complex, dynamic world of museums and art galleries and discourses of new museology, feminism, transformative learning, public pedagogy, and decolonisation.Museums and art galleries are important institutions for a number of interconnected reasons.Firstly, they are pervasive.Approximately 2300 of these institutions exist across Canada alone and thousands more worldwide.Secondly, they are visited daily by millions of adults, many of whom trust almost implicitly what these highly authoritative, seemingly objective, and highly intellectual storytellers show and tell them about the world and themselves (e.g.Gordon-Walker, 2018;Janes, 2015).Thirdly, while adults visit museums and art galleries for the purposes of leisure and fun, many come to learn something about history, society, art, nature and culture.Therefore, regardless of genre or theme -ethnographic, aesthetic, scientific, textile, industrial, military, Indigenous, or war, to name but a few -our art and culture institutions play a key educational role.They act as nonformal educators through seminars, guided tours, workshops and various forms of community engagement.Museums and art galleries are also masters of informal teaching and learning.By fusing together objects, artefacts and narratives into exhibitions and displays, these institutions visualize and engulf us into created worlds with extraordinary communicative power.They have, to borrow from Giroux (2004), great power "over how people think of themselves and their relationship to society and to others" (p.59).Yet similar to all practices of power, museums and art galleries are never neutral.The "nature of their work, selecting which objects to collect and whose memories to preserve-or not, deciding whose stories will be told-or not, and not least, defining which voices are worthy of being heard in the great human choir of history speaks of a great deal of power" (Brekke, 2018, p. 268).This power makes museums and art galleries, as this volume illustrates, legitimate spaces of critique and intervention for adult educators.Museums are practised at weaving narrow mainstream accounts of history, culture and people, shaping what people see, understand and accept as reality about the world, themselves, and 'the other' (e.g.Bergsdóttir, 2016;Phillips, 2011).Even when our own experiences challenge their limited, ideological narratives-many are deeply colonial, racist
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 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".