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Record W4386254224 · doi:10.46328/ijres.3211

Biodiversity and Science, Technology, Society and Environment (STSE): Visitor Perceptions at a Science and Natural History Museum

2023· article· en· W4386254224 on OpenAlexaboutno aff
Pedro Donizete Colombo, Martha Marandino, Graziele Scalfi

Bibliographic record

VenueInternational Journal of Research in Education and Science · 2023
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsVisitor patternAssertionContext (archaeology)PsychologyPerceptionPerspective (graphical)Social psychologySociologyPedagogyGeographyVisual artsArchaeology

Abstract

fetched live from OpenAlex

This qualitative study investigated perceptions of STSE topics manifested by adults visiting four biodiversity exhibits at the Royal Ontario Museum (ROM) in Toronto, Canada. Seven visitors were observed by researchers during their visits and completed post-visit interviews. The theoretical perspective on STSE relationships expounded by Pedretti and Nazir (2011) and Steele (2014) was adopted for the analysis portion of this investigation. Results indicate that the exhibits were able to communicate their central messages on the relationship between human beings and the natural world and the relationship between science and society, as well as reveal some of the effects that these messages had on the public. However, results also support the assertion that some of the intended STSE concepts were not perceived by the participants. Moreover, we identified limitations regarding adult visitors’ abilities to perceive science and technology as human activities embedded in a social, historical and moral context.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.058
GPT teacher head0.408
Teacher spread0.350 · 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.

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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