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Record W3161148137 · doi:10.29173/pathfinder38

Indians in the Database

2021· article· en· W3161148137 on OpenAlexaffvenueabout
Christian Isbister

Bibliographic record

VenuePathfinder A Canadian Journal for Information Science Students and Early Career Professionals · 2021
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIndigenousTerminologySubject (documents)VocabularyExploratory researchFeelingPsychologySociologyLinguisticsLibrary scienceSocial psychologySocial scienceComputer science

Abstract

fetched live from OpenAlex

The goal of this exploratory research study is to better understand how students in the Faculty of Native Studies at the University of Alberta relate to terminology for Indigenous peoples in Canada, namely Indian, in controlled vocabulary subject headings. The language used in controlled vocabularies to describe resources about Indigenous peoples does not always reflect terms Indigenous peoples use to describe themselves, leading to a disconnect between users and subject headings. Although this issue is beginning to enter academic discourse alongside reconciliation efforts, to date no research study has examined how students react to this issue. In this study interviews were conducted with five students from the Faculty of Native Studies to better understand how they relate to terminology. Students reported feeling uncomfortable at being forced to use language they saw as racist or insensitive. Future research should be conducted to better understand student relationships with subject headings, particularly at different institutions

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)0.009
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.078
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.018
Science and technology studies0.0030.001
Scholarly communication0.0080.004
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0470.026

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 head0.033
GPT teacher head0.342
Teacher spread0.309 · 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
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
Published2021
Admission routes3
Has abstractyes

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Same venuePathfinder A Canadian Journal for Information Science Students and Early Career ProfessionalsSame topicNatural Language Processing TechniquesFrench-language works237,207