Becoming a Better ME: Chinese science museum educators' expectations for professional growth
Bibliographic record
Abstract
Abstract Museum educators' (MEs) visions and desires for career development represent an idealized and aspirational identity of being better museum educational professionals. As part of a larger project that explored museum educators' self‐concept as education professionals in China, this preliminary study explored 23 Chinese science museum educators' thoughts and ideas about their imagined professional identity in terms of describing personal desires for professional development pathways. Informed by a Possible Selves theoretical perspective, museum educators in this study elucidated five hoped‐for professional development approaches, including cross‐departmental communication, external communication, formal training in an engaging approach, peer support, and self‐regulated learning. Their expectations for professional growth, to a large extent, were derived from personal reflection and social comparison on the basis of their past work experiences in museum institutions. Therefore, their imagination about future professional development was deeply influenced by the complicated sociocultural and political contexts in which they lived and worked. As the professionalization of science museum educator work in China is at a relatively early stage of emergence, this study provides insights that may help scaffold and direct professionalization efforts of museum education practices in China, and other countries and regions with similar contextual situations.
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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 | 0.004 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".