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Record W4393971238 · doi:10.7202/1104268ar

History in the Age of Abundance? How the Web Is Transforming Historical Research. Ian Milligan. Montreal: McGill-Queen’s University Press, 2019. 328 pp. 9780773556973

2023· article· en· W4393971238 on OpenAlexaffvenueabout
Amir Lavie

Bibliographic record

VenueArchivaria · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicOral History, Memory, Narrative Analysis
Canadian institutionsLibrary and Archives Canada
Fundersnot available
KeywordsQueen (butterfly)Abundance (ecology)Media studiesArtHistoryArt historySociologyBiologyZoologyEcology

Abstract

fetched live from OpenAlex

History in the Age of Abundance? How the Web Is Transforming Historical Research. Ian Milligan. Montreal: McGill-Queen’s University Press, 2019. 328 pp. 9780773556973. Un article de la revue Archivaria (Numéro 95, spring 2023, p. 6-194) diffusée par la plateforme Érudit.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.337

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.006
Science and technology studies0.0030.010
Scholarly communication0.0110.013
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0540.012

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 head0.077
GPT teacher head0.249
Teacher spread0.172 · 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.

Study designNot applicable
Domainnot available
GenreReview

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 routes3
Has abstractyes

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