MétaCan
Menu
Back to cohort
Record W2909420809 · doi:10.3138/jelis.59.4.2018-0013

Education for the Common Good: A Student Perspective on Including Social Justice in LIS Education

2018· article· en· W2909420809 on OpenAlexaffabout
Davin Helkenberg, Nicole Schoenberger, S. A. Vander Kooy, Amanda Pemberton, Karim Ali, S.P. Bartlett, Jillian Clair, Sydney Crombleholme, Alison Dee, Kristy DePierro, Tim Greenwood, Meagan Lobzun, Cassandra Petersen, Sabrina Redwing Saunders, Mary Tarzi, Kristyn Ward, Stacey Zip

Bibliographic record

VenueJournal of Education for Library and Information Science · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsWestern University
FundersInstitute for Humane Studies, George Mason UniversityAmerican Library Association
KeywordsAccreditationCurriculumSociologySocial justiceContext (archaeology)Perspective (graphical)PedagogyEconomic JusticeRelevance (law)Library scienceEngineering ethicsPolitical scienceSocial scienceEngineeringLawComputer science

Abstract

fetched live from OpenAlex

This paper was produced as a collaborative project by a Progressive Librarianship class at an ALA-accredited Masters of Library and Information Science (MLIS) program located in Canada. Recent research in LIS has identified a need for issues of social justice to be discussed more prominently in LIS education. From a uniquely student perspective, the authors suggest how MLIS programs can incorporate social justice as a key component in LIS education. Specifically, they encourage pedagogy that supports critical thinking on issues of social justice and provides scaffolding for progressive change for the common good within a library context. This includes where social justice should appear in the LIS curriculum, who should teach about social justice, what topics are currently of relevance, and suggestions on key strategies for progressive change that can be taught in LIS education.

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)0.011
metaresearch head score (Gemma)0.009
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: none
Teacher disagreement score0.025
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0200.025
Scholarly communication0.0250.014
Open science0.0020.016
Research integrity0.0070.013
Insufficient payload (model declined to judge)0.0040.001

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 head0.044
GPT teacher head0.418
Teacher spread0.374 · 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

Citations15
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Education for Library and Information ScienceSame topicLibrary Science and AdministrationFrench-language works237,207