Bibliographic record
Abstract
An interview study was conducted with women who are active in the animal rights and welfare movements. The purpose of conducting the research is to contribute to the existing literature on women who rescue, save, and/or protect animals and who also act as teachers and possibly role models for those people with whom they come in contact through their rescue work. Another purpose of the research is to determine whether the thoughts, values, and practices of the participants correspond with ecofeminist theory. The researcher interviewed, in one-on-one sessions, six women who share philosophies and beliefs with regard to animals and their need to be rescued. In addition, two women were interviewed (in a similar manner to the first group of women) but who act and work within an alternative framework with regard to their attitudes, philosophies, and beliefs toward animals. The research method used in this study is snowball sampling. By conducting interviews and giving the women the opportunities to express their voices, the researcher explored the significant and influential experiences and motivations in the lives of women who sincerely care and worry about animals, and who, through their actions and initiatives, demonstrate their care through rescuing, saving, and/or protecting animals from dangerous and life threatening situations. The researcher has attempted to determine what these women try to accomplish in their rescue work, whether and how they act as teachers and role models, why they work toward rescuing, saving, and/or protecting animals, and the extent to which their voices have been heard.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".