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Record W4385067225 · doi:10.1108/lm-05-2023-0037

North: the Canadian shared print network/Nord: Réseau canadien de conservation partagée des documents imprimés

2023· article· en· W4385067225 on OpenAlexaffabout
Joseph Hafner

Bibliographic record

VenueLibrary Management · 2023
Typearticle
Languageen
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsMcGill University
Fundersnot available
KeywordsOriginalityPresentation (obstetrics)Work (physics)Library scienceCreating shared valueWorld Wide WebComputer scienceSociologyPolitical scienceEngineeringPublic relationsSocial science

Abstract

fetched live from OpenAlex

Purpose This paper was presented at the Kuopio 2022 Conference in Vienna, Austria on September 7, 2022, to show the work in Canada to create a Canadian shared print network and to share information about the projects that new group is working on with their partners. Design/methodology/approach This was a case study giving information about the North/Nord Canadian Shared Print network, the history of the group and where the group will go next. The author has been part of this project since it began and continues to work on this at the time of publication. Findings This paper shares information about the network and three projects they are working on along with information about shared print projects in Canada and shows that this Canadian network has a unique approach among other current shared print networks. Originality/value This is the first international presentation about this new shared print network and their original approach to shared print because of their focus on Canadiana.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.991
Threshold uncertainty score0.362

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0240.005
Scholarly communication0.0090.003
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0410.003

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 head0.105
GPT teacher head0.259
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.

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
Published2023
Admission routes2
Has abstractyes

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