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Understanding the Effectiveness of Demonstration Programs

2015· article· en· W4256639374 on OpenAlexaff
Allison Price, Emily R. Boeving, Marisa A. Shender, Stephen R. Ross

Bibliographic record

VenueJournal of Museum Education · 2015
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsVisitor patternPsychologyAffect (linguistics)Social psychologyComputer scienceCommunication

Abstract

fetched live from OpenAlex

This project sought to understand guest engagement during great ape demonstrations conducted at the Regenstein Center for African Apes in the Lincoln Park Zoo. We were interested in how these demonstrations engaged audiences, relative to a non-demonstration-viewing experience, as well as how they compared to each other. In 2012 and 2013, we conducted 336 visitor surveys, collecting data before and directly following both the research and training demonstrations, to measure knowledge gain and affective change. We also compared visitor responses to assess potential variances in the ability of these demonstrations to affect both knowledge and attitude shifts about apes. Preliminary results indicated that the presence of either demonstration program enhanced the visitor's knowledge and caring attitudes; however, there was no significant difference between the two program types. In sum, these results demonstrated the capability of interpreted demonstrations in a zoo setting and the relative ability of research- and husbandry-themed programs to engage audiences. These were critical reflections for us as we embarked on a new research- themed exhibit on zoo grounds.

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)0.020
metaresearch head score (Gemma)0.096
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.096
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.187
GPT teacher head0.396
Teacher spread0.209 · 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 designObservational
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

Citations21
Published2015
Admission routes1
Has abstractyes

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