MétaCan
Menu
Back to cohort
Record W2750675475 · doi:10.26443/el.v18i1.62

The (Bene)fits of Compiling a Specialized Database

2017· article· en· W2750675475 on OpenAlexaboutno aff
Anne Wade

Bibliographic record

VenueEducation Libraries · 2017
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsDatabaseComputer scienceVariety (cybernetics)SoftwareCompilerWorld Wide WebArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

The Centre for the Study of Classroom Processes (CSCP) at Concordia University in Montreal recently completed a three year project to compile a specialized database on the successful pedagogical approach of cooperative learning.Using the BiblioLinks software, the results of CD-ROM searches executed on a variety of source databases, were transferred into an in-house database. Utilizing the ProCite bibliographic management software, a package designedto store bibliographic information, the database was customized according to the needs of the CSCP. The benefits of establishing a specialized database included the ability to perform extensive searches on multidisciplinary sub-topicswithin the area of cooperative learning. However a number of problems occurred throughout the course of compiling the database, the majority of which related to limitations with the hardware and software. This article provides a chronology of those problems. Issues related to copyright and the publication of this database in the form of a comprehensive bibliography, are also addressed.

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)0.021
metaresearch head score (Gemma)0.064
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.064
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0180.017
Science and technology studies0.0030.001
Scholarly communication0.0130.012
Open science0.0040.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0230.025

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 head0.095
GPT teacher head0.440
Teacher spread0.345 · 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
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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueEducation LibrariesSame topicInnovative Teaching and Learning MethodsFrench-language works237,207