DEVELOPING AND LAUNCHING AN ONLINE HUB TO FACILITATE THE EXCHANGE OF RESEARCH KNOWLEDGE IN EDUCATION: THE CASE OF THE OERE
Bibliographic record
Abstract
This paper outlines the process by which the Ontario Education Research Exchange (OERE), part of the Knowledge Network of Applied Education Research, developed and launched an online hub of education research summaries to facilitate greater use of research by stakeholders in the field of education. The project is an effort in knowledge mobilization funded by the Ontario Ministry of Education to help increase the use of research to inform policy and practice in Ontario. The paper begins with an outline of the background and history of the project. Next, the three main components of the project are outlined— collecting/writing the summaries and creating the inventory, putting together the peer review process, and creating the online hub for storing and sharing the summaries and facilitating the peer review process. This paper provides useful information that can be translated to similar projects with the goals of summarizing, storing, and/or sharing research with a broad audience.
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 | 0.074 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".