The creation of a grey literature database for the British Columbia Environmental and Occupational Health Research Network (BCEOHRN): lessons learned and next steps
Bibliographic record
Abstract
This paper outlines the steps that led to the creation of a grey literature database for the British Columbia Environmental and Occupational Health Research Network (BCEOHRN), particularly an MLIS student’s involvement in the project during the summer of 2006. The paper makes suggestions for the continued involvement of a professional health librarian as the project grows in size and importance for BCEOHRN researchers. BCEOHRN is one of eight networks funded by the Michael Smith Foundation in early 2005, as part of its Networking Infrastructure Program. Its aim is to “build capacity, facilitate and enhance B.C.’s ability to address health issues, and align health research in the province with national and international research and funding priorities to improve competitiveness for external funding” [1]. As part of this initiative, BCEOHRN works specifically to support research in occupational and environmental health in British Columbia. The scientific director of BCEOHRN, Dr. Susan Kennedy, a professor in the School of Environmental and Occupation Hygiene at the University of British Columbia, ambitiously supports new and innovative initiatives to put BCEOHRN at the forefront of the eight networks. One of the network’s goals for 2006 was to create an environmental and occupational health-related grey literature database, which led to the author’s involvement in the project.
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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 | 0.209 | 0.317 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.041 | 0.048 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.028 | 0.020 |
| Open science | 0.009 | 0.019 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".