Bibliographic record
Abstract
JCPH – Vol. 67, no 4 – juillet–aout 2014 324 other collaborative healthcare settings, the plan must be focused, the efforts to achieve specific goals responsive to the environment and our resources, and the expected outcomes defined and measurable. To this end, CSHP 2015 has been a major project for the organization over the past few years and is now nearing culmination. Yet the measure of our future success as both an organization and a profession remains to be determined. With the end of one phase, we must develop a new plan for the direction of CSHP. In the coming months, the organization will begin developing new goals and directions, beginning with the CSHP Strategic Planning Session in St. John’s, Newfoundland and Labrador, in early August. I look forward to joining the CSHP Executive team for my term as one of CSHP’s Presidential Officers, helping to chart the future course of the organization and attain further success. I hope that you too will do your part as an individual member to support CSHP along this future path. Whether you perceive your efforts as large or small, every contribution counts. Although the Nike slogan “Just do it” is popular and subscribed to by many (including, at times, myself ), I would prefer to say “Start planning and then do it.” What is your plan?
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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.036 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.038 | 0.009 |
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".