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Record W2152804921

Synergies, OJS, and the Ontario Scholars Portal

2008· article· en· W2152804921 on OpenAlexaffabout
Michael Eberle‐Sinatra, Lynn Copeland, Rea Devakos

Bibliographic record

VenueElpub digital library · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsUniversity of TorontoSimon Fraser UniversityUniversité de Montréal
Fundersnot available
KeywordsContext (archaeology)Digital scholarshipDigital libraryResource (disambiguation)World Wide WebScholarshipComputer sciencePublishingScholarly communicationKnowledge managementLibrary sciencePolitical scienceGeography
DOInot available

Abstract

fetched live from OpenAlex

This paper introduces the CFI-funded project Synergies: The Canadian Information Network for Research in the Social Sciences and Humanities, and two of its regional components. This four-year project is a national distributed platform with a wide range of tools to support the creation, distribution, access and archiving of digital objects such as journal articles. It will enable the distribution and use of social sciences and humanities research, as well as to create a resource and platform for pure and applied research. In short, Synergies will be a research tool and a dissemination tool that will greatly enhance the potential and impact of Social Sciences and Humanities scholarship. The Synergies infrastructure is built on two publishing platforms: ?rudit and the Public Knowledge Project (PKP). This paper will present the PKP project within the broader context of scholarly communications. Synergies is also built on regional nodes, with both overlapping and unique services. The Ontario region will be presented as a case study, with particular emphasis on project integration with Scholars Portal, a digital library

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)0.004
metaresearch head score (Gemma)0.009
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 score0.989
Threshold uncertainty score0.404

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.012
Science and technology studies0.0100.007
Scholarly communication0.0110.007
Open science0.0010.010
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0300.004

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 head0.024
GPT teacher head0.166
Teacher spread0.142 · 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
Published2008
Admission routes2
Has abstractyes

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