Podcasting as a Dissemination Method for a Researcher-Practitioner Partnership
Bibliographic record
Abstract
Researcher-practitioner partnerships (RPPs) present opportunities to conduct studies that support evidence-based decision-making for participating school divisions (Coburn, Penuel, & Geil, 2013). Doing this work effectively requires ongoing input from key stakeholders, attention to the local impact of the research, and targeted dissemination to audiences who can benefit from the findings (Tseng, 2012). Research dissemination methods typically include written reports, but constantly evolving media platforms show promise for sharing findings in engaging and innovative ways (Voithofer, 2005). This paper discusses the development and apparent impact of a podcast for a metropolitan RPP to disseminate research findings and other information pertinent to the priorities of partnering school divisions, with implications for broader conversations about exploring issues in public, PK-12 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 | 0.174 | 0.277 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".