Teaching in Situ : Nonformal museum education
Bibliographic record
Abstract
Abstract: Museum education is one of the most ubiquitous forms of nonformal education and one of the least researched, particularly when it comes to understanding the role of the nonformal museum educator (docent). In response to this concern, this qualitative study explored how docents made meaning of their nonformal museum education practice. Through the use of observations, visitor feedback, and interviews with docents, a much more complex picture is revealed of nonformal education than what historically has been reported in the literature. Furthermore, the findings have significant implications for teaching in museum settings. Resume L’education dans les musees est l’un des plus omnipresents formes de l’education non formelle, et l’un des moins etudie, particulierement lorsqu’il s’agit de comprendre le role de l’educateur au musee (guide). Cette etude qualitative a examine comment les guides ont comprendu la pratique d’education dans le musee. A l’aide des observations, les reaction des visiteurs et les entretiens avec les guides, une peinture beaucoup plus complexe est revelee de l’education nonformelle que ce qui a historiquement ete annonce dans la litterature. En outre, les conclusions ont des implications significatives pour enseigner dans les cadres des musees.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".