Efficacy of the Cultivating Knowledge Through Professional Reading Project to Promote Research-Informed Practice Among K-12 Leaders
Bibliographic record
Abstract
Academic research, if mined judiciously, has the potential to enrich K-12 leaders’ professional practice. Yet, K-12 leaders remain skeptical about the value of research to inform their work (Davis, 2007). The purpose of this paper is to report findings from a study of the efficacy of the Cultivating Knowledge Through Professional Reading (CKPR) project. Designed for and instituted in one K-12 Canadian district, CKPR was designed to enhance leaders’ engagement with academic research to inform their practice. Most participants agreed that academic research is an important source of information to support their practice; however, few reported that they regularly consulted research prior to the CKPR project. Seventy-nine percent of respondents noted that research shared via CKPR was usually or always relevant to their work and just over half noted that this made it more likely that they would consult research to support their practice. This article surfaces additional findings along with implications for enhancing K-12 leaders’ uptake of research to inform their practice.
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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 | 0.046 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".