Special Issue on Research Impact in Education
Bibliographic record
Abstract
Amid increased calls for research use in education policy and practice are increased calls for researchers and their research to have impact-an issue experienced globally.After several decades of the study of research use and knowledge utilization, there is a shift in how education research is talked about, and, increasingly, how its evaluation is considered.Motivated by observations of this shift and the recent emergence of research impact in the context of U.S. education, this special issue focuses on scholarship that advances thinking about research impact both conceptually-in the presentation of frameworks and strategies-and empirically-through case studies across multiple contexts.The first piece in the collection is an editorial monograph, "Wordplay or Paradigm Shift: The Meaning of 'Research Impact'," that draws on the testimony of thought leaders in the U.S. education system, offering a conceptual frame for the issue and highlighting several themes and tensions associated with research impact.These issues were front and center in the call for proposals and are addressed in the collection of articles that constitute this special issue. A conceptualization of what it means for research to have impactAcross this volume, impact is taken up in different ways, from changes in policy and practice to changes in student outcomes.In "Exploring Teachers' Conceptual Uses of Research as Part of the Development and Scale Up of Research-Informed Practices," Jane Flood and Chris Brown describe an intervention aimed at creating research-informed teaching practice.This study offers evidence of how research can impact prac- Editorial Special Issue on Research Impact in Education
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.017 | 0.010 |
| Insufficient payload (model declined to judge) | 0.095 | 0.027 |
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".