Education for the Common Good: A Student Perspective on Including Social Justice in LIS Education
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.025 |
| Scholarly communication | 0.025 | 0.014 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".