The grizzly hunt in British Columbia : an ecofeminist evaluation of environmentalists' attitudes toward women in relation to precautionary evidentiary requirements
Bibliographic record
Abstract
The grizzly hunt in British Columbia is an example of a case where the precautionary principle may be invoked.The precautionary principle is located within the New Environmental Paradigm.Based on these connections, this research uses the example of the grizzly hunt to investigate the relationship between environmentalist and feminist attitudes and the evidentiary requirements for precautionary behaviour.Drawing on ecofeminist theory, this research asks if a predisposition toward an environmentalist paradigm also predisposes one toward a feminist paradigm.Participants were 48 self-identified hunters, scientists and activists.The New Environmental Paradigm (NEP) scale and the Attitudes toward Women (AWS) scale were administered to participants to examine their attitudes toward the environment and women's rights.The participants scored highly on both scales, demonstrating positive attitudes toward both environmentalist and feminist issues.However, there was no relationship between responses to the two scales.To further test the association between environmentalism and feminism, scenarios concerning the grizzly hunt and the use of tamoxifen as a chemopreventative for breast cancer were presented to the participants.For each issue, participants were asked to rate four scenarios and choose which one would lead them to refrain from action, i.e., invoke the precautionary principle.Scenarios varied by the certainty of evidence iv presented (high or low) and the expertise of the source (high or low).Data collected from the grizzly bear hunt scenarios determined that participants favoured evidence from the low uncertaintyhigh expertise scenarios.When participants responded to scenarios concerned with the use of tamoxifen as a chemopreventative for breast cancer, they did not choose any one of the four scenarios more often than would be expected by chance.I concluded that individuals well versed in an environmental issue responded to this issue using expertise derived from their special relationship with this issue in contrast to feminist issues, where their responses were based on personal experience and general knowledge rather than their expertise.The relationship of this finding to the precautionary principle and role of paradigms in this process is discussed. DedicationFor my Uncle Garry, whose last words to me were, "I think we need to talk more about that thesis of yours.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".