Using Logic Analysis to Evaluate Knowledge Transfer Initiatives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Models that shift more responsibility onto researchers for the process of incorporating research results into decision-making have greatly gained in popularity during the past two decades. This shift has created a new area of research to identify the best ways to transfer academic results into the organizational and political arenas. However, evaluating the utilization of information coming out of a knowledge transfer (KT) initiative remains an enormous challenge. This article demonstrates how logic analysis has proven to be a useful evaluation method to assess the utilization potential of KT initiatives. We present the case of the evaluation of the Research Collective on the Organization of Primary Care Services, an innovative experiment in knowledge synthesis and transfer. The conclusions focus not only on the utilization potential of results coming out of the Research Collective, but also on the theoretical framework used, in order to facilitate its application to the evaluation of other knowledge transfer initiatives.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it