Are We in a Pickle? Rethinking the World of Research and User Interaction
Bibliographic record
Abstract
The lion's tail and knowledge boundaries are two analogies referred to in the lead essay by Lindstrom, MacLeod and Levy. These may be helpful but require slight readjustment. Grabbing onto the lion's tail implies one reality and one intersection point, whereas the old analogy of the blind men and the elephant shows that various perspectives are required. Integrated knowledge translation refers to user involvement throughout the research process. Participatory models are one form of integrated knowledge translation, but caution is required to help maintain the knowledge boundaries. There is the real danger of one group becoming "pickled," or having unbalanced osmotic pressure from another group, resulting in destroyed "cell wall" boundaries. Neither researchers nor users should morph into each other but should, rather, fulfill unique roles within a respectful, trusted research relationship. Lessons learned at the Manitoba Centre for Health Policy teach us that collaborative health services research takes time, money, mutual understanding and respect (including respect from academic institutions for this paradigm of research). This requires a dedicated centre of core group scientists willing to devote the necessary time. Diffused networks may not be stable enough to maintain the long-term relationship building required for the intersection of researchers and decision-makers.
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 | 0.056 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.088 |
| Scholarly communication | 0.030 | 0.065 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.090 | 0.124 |
| Insufficient payload (model declined to judge) | 0.006 | 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".