Reflecting back, looking forward: A content analysis of scientific programs from the 2013–2016 Canadian Sex Research Forum annual conferences
Bibliographic record
Abstract
The vision of the Canadian Sex Research Forum (CSRF) is to be Canada's leading organization dedicated to interdisciplinary, theoretical, and applied sexuality research. We sought to determine the composition of four previous CSRF Annual Conference (2013–2016) scientific programs. We double-coded 356 abstracts on first author region, discipline, and faculty status; presentation format (oral/poster); and several non-exclusive yes/no questions regarding study populations, topics, and methods. We calculated odds ratios (OR) to assess trends (per year) and likelihood of oral versus poster presentation. Most of authors were from psychology (86.5%), although this decreased over time (98.1% to 80.5%). Most abstracts used quantitative methods (82.9%) and there was a decrease over time in abstracts using qualitative (26.4% to 16.3%) and experimental (17.0% to 7.3%) methods. For study population and topic, there were increases over time in clinical population foci (7.6% to 23.6%) and decreases in race/ethnicity foci (3.8% to 0.8%) and methods topics (18.9% to 5.7%). Half of the abstracts were oral presentations (44.9%), which were more frequently awarded to faculty (81.1% vs. 38.6%), sexual practice topics (50.7% vs. 40.8%), relationship topics (52.3% vs. 40.7%), methodology topics (50.0% vs. 44.2%), and theory papers (71.4% vs. 43.3%). Oral presentations were less frequently awarded to single sex/gender populations (36.7% vs. 48.4%), student-only populations (35.3% vs. 51.2%), race/ethnicity foci (20.0% vs. 45.5%), and quantitative methods (43.4% vs. 52.5%). To achieve CSRF's vision of “interdisciplinary, theoretical, and applied research,” we must undertake intentional strategic action (e.g., more content from non-psychology disciplines, more qualitative methods).
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 | 0.064 | 0.296 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.034 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".