The <i>Journal of Comorbidity</i> Affiliates with the Scottish School of Primary Care
Bibliographic record
Abstract
The Journal of Comorbidity is pleased to announce a new partnership with the Scottish School of Primary Care (SSPC). The SSPC is a virtual school comprising all Scottish academic departments with significant primary care research output. This currently includes the Universities of Aberdeen, Dundee, Edinburgh, Glasgow, Stirling, and St. Andrew’s. This is the second important partnership that the journal has formed to strengthen ties with professional primary care networks with an interest in comorbidity and multimorbidity research. It is anticipated that this new collaboration will increase the journal’s reach and help cultivate research, discussion, and knowledge about comorbidity and multimorbidity. The Scottish School of Primary Care Since its inception in 2000, the SSPC has established a reputation for research excellence, attracting new research funding to Scottish universities. The school aims to support the development of a sustainable, equitable, high-quality primary care service that meets the needs of the people of Scotland. Working towards this vision, the SSPC’s current strategic objectives are to: Inform key stakeholders by collating relevant available national and international evidence, as well as actively contributing to the growing evidence base Support the continuing growth of academic primary care in Scotland Promote Scottish academic primary care internationally. Journal of Comorbidity 2017;7(1):112–113
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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 | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.167 | 0.031 |
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".