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Record W4238592682 · doi:10.32920/ryerson.14658054.v1

Subject matter identification by means of community outreach

2021· preprint· en· W4238592682 on OpenAlexaffabout
Erin Cral

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsOutreachSubject (documents)Subject matterSection (typography)Identification (biology)Library scienceSociologyComputer sciencePolitical sciencePedagogyLawEcologyCurriculum

Abstract

fetched live from OpenAlex

The purpose of this practical thesis project was to create a guidebook for collecting subject-based information gathered through community participation and collaboration. Specifically, this involved collecting subject descriptions for a photograph collection based on an organized and planned meeting with local residents familiar with the contents of the images. All fieldwork was completed over a nine-week period, from June 5 through August 2, 2006, at the Bruce County Museum and Cultural Centre, Southampton, Ontario with the John H. Scougall Collection. 105 images were selected for discussion and subject identification by community members. What follows is a guidebook to inform others how to carry out such a project. Each section begins with general comments and ideas, followed by specific examples of what took place with the Scougall Collection and the participating residents of Kincardine.

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)0.008
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.064
Threshold uncertainty score0.214

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0090.002
Scholarly communication0.0040.003
Open science0.0020.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0640.028

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 head0.136
GPT teacher head0.265
Teacher spread0.129 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes2
Has abstractyes

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