Bibliographic record
Abstract
The purpose of this study was to investigate the attitudes of scientists and lay people participating in National Cancer Institute of Canada (NCIC) grant review panels towards the inclusion of non-scientists in the review process. Questionnaires were sent to the 126 scientists and 24 lay panelists who participated in NCIC's grant reviews in 1998. Survey topics included lay member selection, the role of the lay panelist and suggestions for improving the process. Data were analyzed qualitatively, and quantitatively using SPSS. Sixty-one of the 126 scientists (48.4%) and 16 of the 24 lay panelists (66.7%) completed the survey. Female scientists were significantly more supportive than male scientists of the selection of cancer patients/survivors/advocates as lay members (p = 0.01), but overall their responses were more similar to those of their male colleagues than of the lay respondents. There were significant differences between the lay and scientist respondents on lay member responsibilities (p = 0.01), the format of lay grant review (p = 0.04), lay member contribution to panel discussion (p = 0.01), and understanding of the lay role (p = 0.02).
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 | 0.138 | 0.305 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".