Bibliographic record
Abstract
Many businesses and organizations of all types have adopted ethics codes or codes of conduct. Examples relevant to geoscience include the Cape Town Statement on Geoethics of the International Association for Promoting Geoethics, the Scientific Integrity and Professional Ethics policy of the American Geophysical Union, and the Joint EGU-AGU Statement of principles for a code of ethics for the geosciences. The Government of Canada is implementing a Science Integrity Policy across its science-related Departments. Successful implementation of such policies can be challenging and many breaches have been and continue to be reported. Humans make ethical or unethical decisions and understanding the sociological processes that are involved and applying this knowledge to the implementation of ethics codes may improve their success rates. This paper analyzes these sociological processes through the lens of symbolic interactionism theory. In spite of its somewhat forbidding name, the theory is actually quite simple. It shows how interactions between people lead to the meanings they give to other people, organizations and things. It describes how these meanings lead to the interpretation of situations, and how groups arrive at normative decisions based on this interpretation. These normative decisions involve ethical considerations. The paper describes the approach and seeks audience feedback on a proposed survey of the members of the International Association for Promoting Geoethics to collect empirical evidence on which to base a symbolic interactionist approach to effective implementation of ethics codes in geoscience.
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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 | 0.064 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.050 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.016 | 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".