Towards collective intelligence in a national community of physicians
Bibliographic record
Abstract
Collective intelligence is shared or group intelligence that emerges from collaborative effort. We propose to harness collective intelligence through the specific tasks of producing and sharing constructive comments on synopses of clinical research, disseminated to a national community of physician members of the Canadian Medical Association. This proposal uses the power of many to bring the collective wisdom and resources of a community of physicians back to the individual. Building on an active program of continuing education to raise awareness of synopses of new clinical research for physicians, we describe the characteristics of a new system to be built upon an existing web platform. This new platform will offer physicians an opportunity to read and share their comments on Patient Oriented Evidence that Matters (POEMs), with other physicians, which will stimulate collective intelligence. In turn, this will further benefit the education of these physicians and help to improve the decisions they make in everyday clinical practice. Knowing about this endeavour may be of benefit to the community of information professionals in multiple fields, who seek to improve their use of evidence-based abstracts of scientific publications and experience-based (information users’ comments) information in their daily work.
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 | 0.033 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".