Bibliographic record
Abstract
Abstract In this paper, I address some of the unique challenges of studies of learning in museums through a microanalytic case study of meaning‐making among a group of youth and a curator. Through an examination of youths' forms of participation in one exhibit, I illustrate local meaning making achieved through multiple modalities—by doing, talking, and the manipulation of the exhibit. In turn, I show how multiple on‐going dialogues come to interact and constitute talk and action at the science exhibit underlining the idiosyncratic nature of meaning‐making. While the dialogue examined in this paper may be considered as a rather unremarkable event in terms of learning, it underlines that the study of meaning‐making entails a focus on more than mere conversations in situ in that verbal and nonverbal interactions need to be considered simultaneously. Furthermore, the analysis suggests that museums may be best seen as one among many resources for science literacy development whose impact can only be understood through an assessment of learning trajectories over time and across space. Suggestions are made for museum design and future studies of learning in consideration of the issues raised. © 2004 Wiley Periodicals Inc. Sci Ed 88: 223–247, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/sce.10117
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".