Book Review: Reflective Teaching Effective Learning: Instructional Literacy for Library Educators
Bibliographic record
Abstract
Reflective Teaching, Effective Learning: Instructional Literacy for Library Educators.Booth's thoughtful approach to delivering information literacy in higher education has influenced my own practice and I welcome the opportunity to share a review of it.The book is comprised of two parts.The first part focuses on various aspects of instructional literacy.Booth scrutinizes teaching effectiveness, at one point attempting to answer the question, "What makes a good teacher?"(p.5).One intriguing answer the author asserts is appealing to the self-interest of the learner who is implicitly asking, "What's in it for me?"-the WIIFM principle (p.13).She recommends learning from a network of library educators who can support one's efforts to become a teacher characterized by "authenticity" (p.9-10).Furthermore, she suggests cultivating "communities of practice" (p.28), whether in person or online, in an effort to develop and mature as an instructor.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".