Bibliographic record
Abstract
Integrated knowledge translation (IKT) is built upon the premise that involving knowledge users as partners in the research process will result in science that is more relevant to the public and therefore will have greater impact. Drawing on our experiences with a large and multifaceted IKT food allergy research program we highlight the disjuncture between the goals of IKT and the nature of basic science research, most notably the long timelines before research is ready for translation. Our partner consultations concluded that IKT success should be measured in a different way. That is, it should not be about informing an immediate gap in the translation of food allergy findings but about building relationships between our partners, greater awareness, understanding and knowledge about the nature of science and IKT, and ultimately helping to create better policy and science down the road. It is the recognition that it behooves us as scientists to be able to answer those "why" questions. We call for other researchers to consider the success of IKT beyond the short term timelines of any one research project but instead as an avenue to build partnerships, innovate thinking about research questions and to maximize choice and minimize risk for individuals in Canada and beyond affected by food allergy.
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 | 0.119 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.049 |
| Scholarly communication | 0.028 | 0.036 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.010 | 0.037 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".