“Capturing the Outcomes and Impacts of Publicly Funded Research” A Framework for Evaluating Formal Research Networks DRAFT
Bibliographic record
Abstract
Research granting agencies in Canada have increasingly turned to formal research networks (those with an organizational structure and mandate) such as NCEs and MCRIs as a mechanism to meet policy objectives such as collaboration, multi-disciplinarily and more importantly, the linking of researchers and perceived relevant stakeholder communities (industry and population groups). The project by Lewis, Holbrook and Wixted, has developed an approach to evaluate the core policy objective of networks; that is, the networking. Our framework is to conceive of the different stakeholders in formal networks as clusters of actors, rather than as individuals connecting within social networks. From this starting point we have applied concepts from actor-network theory to develop quantitative and qualitative criteria to evaluate how well these formal networks connect researchers to stakeholder communities, and how these networks communicate among all of their stakeholders channels carry information (communications). One important dimension of our work is to consider the effects of strong (established
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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 | 0.211 | 0.266 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".