Are We Done Yet? Reflections on the Sustainability of Knowledge Products
Bibliographic record
Abstract
While collaborative research approaches help ensure that knowledge products resulting from research will be relevant to stakeholders and increase the likelihood that they will be integrated into practice, there has been limited attention given to the supports essential to maintaining knowledge products. Focussing on one research project whose knowledge products are heavily used, in this paper, we discuss the challenges associated with maintaining the integrity of these knowledge products, particularly tensions associated with: (1) lack of alignment of our needs, timelines and resources as researchers with those of community partners; (2) the ongoing need to support the evolution of knowledge products despite the conclusion of funding and project infrastructure and (3) lack of clarity about decision-making responsibility related to the ongoing evolution of these knowledge products. Out of these challenges, we offer recommendations for negotiating the evolution of knowledge products and sustaining the Knowledge to Action (KTA) cycle. These recommendations focus on documenting responsibilities for knowledge product maintenance and communication, assigning expiry dates to knowledge products, identifying secure, long-term repositories for knowledge products and planning for engagement of research partners with lived experience in the maintenance of research products.
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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 | 0.131 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.065 |
| Scholarly communication | 0.035 | 0.060 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.011 | 0.013 |
| 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".