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Record W4312403529 · doi:10.37745/ijhphr.13/vol10n2117

History Museum’s Social Experiences Case Study

2022· article· en· W4312403529 on OpenAlexaboutno aff
Mahmood Niroobakhsh

Bibliographic record

VenueInternational Journal of History and Philosophical Research · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsVisitor patternFeelingAttendanceQuarter (Canadian coin)InterviewPsychologySample (material)PopulationEntertainmentPerceptionData collectionSocial psychologyDemographySociologyGeographyVisual artsSocial scienceArt

Abstract

fetched live from OpenAlex

To assess stability of visitor-level attendance in specific period of time among participants in a daily program and determine social factors affecting people who attend the museum. Participants of the Altona History Museum were interviewed using a personal-interviewing instrument. In each wave of data collection, a cross section of the convenient sample was screened. The central factor was the adaptive social perception of the average visitor of the event with the theoretical propositions. The symbols have limited prevalence in the pursuit of museum, which likewise meant for a quarter of higher educated patrons a style of entertainment. Record numbers of the adults’ interest accompanied by their early years attendances are likely to have the potential to generate a substantial population of regular visitors. Lastly, the subjective issue of good feeling accounts for a significant influence in making them content.

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)0.001
metaresearch head score (Gemma)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.002
Scholarly communication0.0020.001
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 head0.321
GPT teacher head0.387
Teacher spread0.067 · 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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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