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Record W7009208379

Directory of Curriculum Materials Centers and Collections 8th Edition

2023· article· en· W7009208379 on OpenAlexfundaboutno aff

Bibliographic record

VenueD-Scholarship@Pitt (University of Pittsburgh) · 2023
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsnot available
FundersQueens College, City University of New YorkState University of New York CortlandCalifornia State University, SacramentoUniversity of Illinois at Urbana-ChampaignMemorial University of NewfoundlandUniversity of TorontoPenn State Harrisburg, Pennsylvania State UniversityUniversity of Nebraska KearneyYork UniversitySalisbury UniversityOklahoma State UniversityHigh Point UniversityFlorida Gulf Coast UniversityUniversity of CincinnatiNorthern Arizona UniversityUniversity of St. ThomasUniversity of PennsylvaniaNorthern Michigan UniversityUniversity of Northern IowaMiddle Tennessee State UniversityPennsylvania State UniversityJames Madison UniversityUniversity of PittsburghIndiana State UniversityIllinois State UniversityUniversity of Central FloridaLoyola University ChicagoNorth Carolina State UniversityUniversity of Southern Mississippi
KeywordsDirectoryCurriculumCenter (category theory)Section (typography)Information center
DOInot available

Abstract

fetched live from OpenAlex

The 8th Edition of the Directory of Curriculum Materials Center and Collections was compiled by the Curriculum Materials Committee of the Education and Behavioral Sciences Section of the Association of College and Research Libraries, a Division of the American Library Association. It contains data from 112 institutions which have either a curriculum materials center or collection in the United States and Canada. Data includes information about institutional demographics, facilities, staffing, funding, and types of materials selected and cataloged. Multiple figures aggregate the data provided.

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)0.013
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: Other · Consensus signal: none
Teacher disagreement score0.215
Threshold uncertainty score0.718

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0150.032
Science and technology studies0.0030.001
Scholarly communication0.0080.004
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.2150.204

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 head0.016
GPT teacher head0.207
Teacher spread0.190 · 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
GenreOther

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".

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Citations0
Published2023
Admission routes2
Has abstractyes

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