MétaCan
Menu
Back to cohort
Record W3144895089 · doi:10.29173/iasl7810

Social Role of the Librarians of the Federal Institute of Education, Science and Technology

2021· article· en· W3144895089 on OpenAlexvenueno aff
Caroline Becker

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldComputer Science
TopicInformation Science and Libraries
Canadian institutionsnot available
Fundersnot available
KeywordsRelation (database)Public relationsPolitical scienceLibrary scienceInformation scienceSociologyFoundation (evidence)Medical educationPedagogyComputer scienceMedicine

Abstract

fetched live from OpenAlex

The study was carried out through the theoretical foundation about the conceptions and objectives of the Federal Institute of Education, Science and Technology, and also on the social role of the librarians of this educational institute. These Federal Institutes were created in Brazil in 2009 and they offer basic and higher education. This study aims at investigating, analyzing, and understanding if the librarians of the Federal Institutes of Education, Science, and Technology recognize their social roles as professionals that can contribute to the development of cognitive skills with regards to the information in the library’s users. A case study was carried out with all the librarians of the Federal Institutes and questionnaires were the method used for collecting data. It should be noted in the librarians’ answers that they recognize their social roles, and they act according to what they recognize. In their everyday practices, these librarians try to minimize the difficulties that the library’s users face in relation to the search, location, use, assessment, dissemination, and understanding of information.

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)0.006
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.004
Science and technology studies0.0140.005
Scholarly communication0.0070.003
Open science0.0010.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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 designQualitative
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueIASL Annual Conference ProceedingsSame topicInformation Science and LibrariesFrench-language works237,207