Learning critical feminist research: A brief introduction to feminist epistemologies and methodologies
Bibliographic record
Abstract
This article serves as a welcoming introduction to feminist epistemologies and methodologies, written to accompany (and intended to be read prior to) the Virtual Special Issue on ‘Doing Critical Feminist Research’. In recalling our own respective journeys into the exciting field of feminist research, we invite new readers in appreciating the steep learning curve out of conventional science. This article begins by sketching out the emergence of feminist scholarship – focusing particularly on the discipline of psychology – to show readers how and why feminist scholars sought to depart from conventional science. In doing so, we explain the emergence of three main ways of doing and thinking about research (i.e. epistemologies): feminist empiricism, standpoint theory, and the various ‘turn to language’ movements (social constructionism, constructivism, postmodernism, poststructuralism). We then connect the dots between feminist epistemologies, methodologies and methods. We close by offering suggestions to guide the readers in using the Virtual Special Issue on their respective research journeys.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".