The Need for Geoethics Awareness from a Canadian Perspective
Bibliographic record
Abstract
An online survey of Canadian Earth scientists on geoethics—defined as the interconnection between humanity and Earth sciences—asked participants to (1) rate the importance of issues around scientific integrity, social responsibility, aboriginal concerns, corporate ethics, and fieldwork; (2) identify ethical considerations they had observed; and (3) tell us how they were introduced to ethical viewpoints and whether their undergraduate programs had prepared them for ethical decision-making. Despite a small sample size (123 responded to our survey) we observe that most respondents deemed all criteria we listed as important or very important, with the strongest support for health/safety and honest reporting, and the least, but still significant support for criteria linked to aboriginal issues and fieldwork. Many respondents had observed ethical considerations, particularly lack of giving credit and biased representation of information. We find that informal activities like reading and discussions with peers are the most frequent avenues into geoethics, while undergraduate education is not a significant contributor to current geoethics understandings. Although the survey was restricted to Canada, we perceive our survey as providing a glimpse into the larger geoscience community and offer various recommendations on how the geoscience community and public must be made aware of geoethics, not just in Canada.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.031 | 0.012 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".