Bibliographic record
Abstract
Topics and issues in library and information science education pedagogy are commonly discussed in panels, conferences, peer-reviewed articles, professional articles, and dedicated monographs. However, in this abundance of education-oriented discussions, there are several noticeable gaps and omissions. Not always do education-oriented publications involve theoretical grounding that could make them stronger in argumentation and more generalizable to other contexts. Addressing these gaps, the book stands to strengthen the less covered areas of library and information science (LIS) pedagogical thought; it enriches a theoretical foundation of pedagogical discourse and broadens its scope. This volume brings together a collection of essays from LIS educators from around the world who delve into difficult, unpopular, and uncommonly discussed topics—the inglorious pedagogy, as we call it—based on their practice and scholarship. Presenting perspectives from Australia, Canada, China, New Zealand, the United Kingdom, and the United States, each chapter is a case study, rooted not only in the author’s experience but also in a solid theoretical or analytical framework that helps the reader make sense of the situations, behaviors, impact, and human emotions involved in each. The collective thought woven in the book chapters leads the reader through the milestones of (in)glorious pedagogy to a better understanding of the potentially transformative nature and wasted opportunities of graduate LIS education and higher education in general.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".