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Record W6913062254 · doi:10.5683/sp3/vynyuo

Cabinet Conclusions, 1944 to 1979

2025· dataset· en· W6913062254 on OpenAlexaboutno aff

Bibliographic record

VenueBorealis · 2025
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCabinet (room)General partnershipScholarshipPrime ministerProduct (mathematics)

Abstract

fetched live from OpenAlex

The Cabinet Conclusions dataset is based on the public record of the meetings of the Cabinet, which consists of advisors to the Prime Minister of Canada. Also known as Cabinet minutes, Cabinet Conclusions, 1944 to 1979 includes agendas, lists of attendees and summaries of discussions. Library and Archives Canada’s Cabinet Conclusions content has been selectively augmented to demonstrate the potential of data linking by combining them with other resources. This includes the Orders in Council Division of the federal government’s Guide to Canadian Ministries since Confederation and selected MIKAN records. This dataset is the product of a partnership between LINCS and LAC. It combines multiple LAC resources to showcase how the value of online LAC collections can be enhanced using Linked Open Data. LINCS would welcome the involvement of subject-matter experts interested in exploring and potentially refining or augmenting this data. Creators and Contributors Library and Archives Canada Julia Barkhouse, Erin Isaac, Stephanie Pettigrew, Mathieu Sabourin Linked Infrastructure for Networked Cultural Scholarship (LINCS) Susan Brown, Natalie Hervieux, Dani Metilli, Alliyya Mo, Sarah Roger, Zachary Schoenberger Support The Cabinet Conclusions dataset was made possible thanks to the generous support of Library and Archives Canada, the Canada Foundation for Innovation, and the Social Sciences and Humanities Research Council of Canada.

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)0.008
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.263
Threshold uncertainty score0.528

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.017
Science and technology studies0.0030.001
Scholarly communication0.0050.002
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0970.094

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 head0.014
GPT teacher head0.300
Teacher spread0.286 · 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
Published2025
Admission routes1
Has abstractyes

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