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Record W4412834568 · doi:10.1111/cura.12674

成为更好的自己 <sup>1</sup> :中国科学类博物馆教育工作者专业成长愿景与路径研究

2025· article· zh· W4412834568 on OpenAlexaff
Jiao Ji, Wei Guo

Bibliographic record

VenueCurator The Museum Journal · 2025
Typearticle
Languagezh
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPerspective (graphical)Computer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

摘 要 博物馆教育工作者(MEs)对专业发展的愿景展现了一种对职业身份理想化的、且富有抱负的追求,即致力于成为更为卓越的博物馆教育专业人士。作为探究中国博物馆教育工作者自我认知大型项目的组成部分,本研究初步调查了23名中国科学类博物馆教育工作者对理想化的专业身份的认知及专业发展路径的愿景。基于可能自我理论(Possible Selves Theoretical Perspective),本研究中的博物馆教育工作者阐释了五种期望的专业发展路径,分别是:跨部门沟通、外部交流、沉浸式正式培训、同伴支持和自我调节学习。这些路径主要源于他们对以往的博物馆工作经验的深刻反思与社会比较。他们对未来专业发展的愿景深受自身所生活和工作的社会政治文化背景影响。鉴于中国科学类博物馆教育工作者的专业化进程尚处于起步阶段,本研究有望为推动中国以及其他类似背景下的博物馆教育实践专业化提供 参考。

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0070.019
Scholarly communication0.0170.019
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0180.002

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.

Opus teacher head0.042
GPT teacher head0.371
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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