Diversity in geoscience: Participation, behaviour, and the division of scientific labour at a Canadian geoscience conference
Bibliographic record
Abstract
Effective policies promoting diversity in geoscience require understanding of how the values and practices of the community support the inclusion of different social groups. As sites of knowledge exchange and professional development, academic conferences are important culturing institutions that can alleviate or reproduce barriers to diversity in geoscience. This study examines diversity at a 2017 geoscience conference, the joint Canadian Geophysical Union and Canadian Society of Agricultural and Forest Meteorology annual meeting, through observation of participation, presentation content, and behaviour in conference sessions. Across 256 observed presentations, women constituted 28% of speakers, whereas women of colour made up only 5%. Participation rates differed between disciplinary sections, with the most populous sessions (Hydrology and Earth Surface) having the lowest percentage of women. Examination of presentation content reveals that the methods and scholarly contributions of both women and people of colour differed from the majority, suggesting an intellectual division of labour in geoscience. Examination of audience behaviours between presenters reveals how a “chilly climate” can be experienced by women and other marginalized demographics in conferences. We argue that there is more to be done than simply increasing numbers of women or other minorities in geoscientific spaces, and we suggest pathways to making geoscience a more inclusive and democratic pursuit.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".