Bibliographic record
Abstract
The feminist biographical method is an in-depth interpretive methodology that is useful for research in the field of psychology. I believe that this qualitative method is an excellent tool for analyzing individual narratives of participants lives in relation to the larger cultural matrix of the society in which they live. Although an oral interview is often the primary strategy employed for data collection in this methodology, other sources of information such as personal journals, official documents, and cultural texts are also exciting additions to the research. The strengths of the feminist biographical method include the depth, context, and meaning found in the research; the inclusion of women’s experiences and voices in academic research; and the ability to conduct a sociopolitical analysis of potentially marginalized people. In this article, I delve into the feminist biographical method by providing discussion and examples from research in the field, as well as from my own research. I provide the reader with a personal narrative on how-to conduct research using the feminist biographical method. In particular, I delineate the process of researching the lived experiences of women international students in difficult relationships. As a psychological researcher, I encourage others in the field of psychology to consider using the feminist biographical research to add context, depth, and richness to studies involving human participants.
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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 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 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".