We Need To Talk: Improving Dialogue between Social Studies Teachers and Museum Educators
Bibliographic record
Abstract
Researchers have argued for increased collaboration between teachers and museum educators to improve the outcomes of museum education on students; however, significant gaps in understanding between the two remain impediments to effective collaboration. We surveyed fifty-one museum educators, conducted in-depth interviews with ten of these respondents, and analyzed the data with use of an inductive lens. In this article we use a composite dialogue between a museum educator and a teacher to present a series of questions teachers should ask of, and information they should provide to, museum educators. Such questions and information can be used to initiate more effective collaborative relationships that may ultimately improve the quality of museum education for our students. We argue that gaps in museum educators’ understanding about teachers’ needs, objectives, and concerns about museum visits could be bridged if teachers knew what questions to ask and what information to volunteer to museum educators before arranging a museum visit.
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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 | 0.045 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.004 | 0.005 |
| 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".