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Record W4205829668 · doi:10.25071/10315/38629

Mass Capture repository of scanned images

2021· article· en· W4205829668 on OpenAlexaboutno aff
Lily Cho, Émilie Létourneau, Helen Piekoszewski, Angie Wong, Rachel Wong, Biwei Claire Zhang, Chloe Shi

Bibliographic record

VenueYork University Digital Library (York University) · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsnot available
Fundersnot available
KeywordsMicroformComputer graphics (images)ResidenceLibrary scienceComputer scienceDatabaseInformation retrievalVisual artsArtGenealogyGeographyHistoryDemographySociology

Abstract

fetched live from OpenAlex

Consists of scanned and enhanced images from microfilm reels held at Library Archives Canada. Organized in file folders labelled by reel number, these images are of CI 9 documents, state documents issued under the Chinese Immigration Act of 1885. Issued from 1885 to 1953, CI 9 documents include date of birth, place of residence, occupation, identifying marks, known associates, and, significantly, identification photographs. The CI 9 documents were microfilmed in 1963 and the originals destroyed. As part of a SSHRC-funded research project led by Dr. Lily Cho, this data set consists of over xxx reconstituted digital reproductions of CI 9 documents.

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)0.005
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.205
Threshold uncertainty score0.687

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0140.018
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0030.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2050.206

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 head0.010
GPT teacher head0.130
Teacher spread0.120 · 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
GenreDataset

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 routes1
Has abstractyes

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