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Record W2954352106 · doi:10.29173/iasl7169

School Library Researchers in the Digital Age: Understanding Teaching, Research and Service in the Academy

2017· article· en· W2954352106 on OpenAlexvenueno aff
Jennifer Branch-Mueller

Bibliographic record

VenueIASL Annual Conference Proceedings · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsMentorshipPublishingService (business)Medical educationCorporate governanceProductivitySociologyPsychologyLibrary sciencePublic relationsPolitical scienceManagementMedicineBusinessMarketingComputer science

Abstract

fetched live from OpenAlex

This study presents the realities of teaching, research and service for 20 school library faculty members working on six continents. Teaching, research and service expectations vary between institutions, with 19/20 of the faculty members teaching four or more classes per academic year with the numbers of students taught varying from 10 to 300. The faculty members in this study are productive, with 9/20 publishing two or more peer-reviewed media per year. Another six are publishing one peer-reviewed media per year. Service is expected and participants in this study are all involved in faculty governance as well as service to the discipline and the profession. This research provides information to potential and current school library faculty that is helpful for making career decisions, e.g., entry to the profession, career progression, research productivity, and mentorship.

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)0.010
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.974
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.005
Science and technology studies0.0150.018
Scholarly communication0.0260.034
Open science0.0020.014
Research integrity0.0030.005
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 head0.339
GPT teacher head0.439
Teacher spread0.099 · 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 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

Citations1
Published2017
Admission routes1
Has abstractyes

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