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Record W7074158782

The Genre of Love-Me Binders US Military Veterans Documenting Their Service

2023· article· en· W7074158782 on OpenAlexaboutno aff

Bibliographic record

VenueIUScholarWorks (Indiana University) · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPublicationPermissionService (business)Sign (mathematics)Work (physics)Dissemination
DOInot available

Abstract

fetched live from OpenAlex

Archivaria, the journal of the Association of Canadian Archivists, has the following policy related to authors depositing their published articles in institutional repositories: \n \n"Authors of manuscripts accepted for publication retain copyright in their work. They are required to sign the Agreement on Authors' Rights and Responsibilities that permits Archivaria to publish and disseminate the work in print and electronically. In the same agreement, authors are required to confirm that "the material submitted for publication in Archivaria, both in its paper and electronic versions, including reproductions of other works (e.g. photographs, maps, etc.) does not infringe upon any existing copyright." Authors of manuscripts accepted for publication retain copyright in their work and are able to publish their articles in institutional repositories or elsewhere as long as the piece is posted after its original appearance on archivaria.ca. Any reproduction within one year following the date of this agreement requires the permission of the General Editor."

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)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.055
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0110.006
Scholarly communication0.0100.004
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0550.010

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 head0.027
GPT teacher head0.181
Teacher spread0.154 · 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
Published2023
Admission routes1
Has abstractyes

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