Librarians and Teachers as Research Partners: Reshaping Practices Based on Assessment and Reflection
Bibliographic record
Abstract
As critical partners in shaping quality learning experiences, school library media specialists have a major stake in examining their teaching practices through the lens of actual student behaviors. Empowerment results when they collaborate with fellow teachers in implementing strategics, reflecting on the results, and sharing them with the professional community. This article focuses on the transformative nature of practitioner research. It describes a multi-year project to identify key components of effective teaching in collaborative elementary school classroom-library settings, and to translate this knowledge into practitioner-facilitated professional development alternatives. A summary of this article was presented at the International Research Symposium sponsored by the Center for International Scholarship in School Libraries, Rutgers University, convened in New York, April 28-29, 2005. The Symposium was funded by a grant from the US Institute of Museum and Library Services.
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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 | 0.231 | 0.290 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.013 | 0.040 |
| Scholarly communication | 0.030 | 0.034 |
| Open science | 0.006 | 0.039 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".