Standpoint Theory and the Psy Sciences: Can Marginalization and Critical Engagement Lead to an Epistemic Advantage?
Bibliographic record
Abstract
Abstract As participatory research practices are increasingly taken up in health research, claims related to experiential authority and expertise are frequently made. Here, in an exploration of what grounds such claims, we consider how feminist standpoint theory might apply to the psy sciences (psychiatry, psychology, psychotherapy, psychoanalysis, and so on). Standpoint theory claims that experiences of marginalization and critical engagement can lead to a standpoint that offers an epistemic advantage within a domain of knowledge. We examine experiences of marginalization and critical engagement in the mental health system, as well as evidence for epistemic advantages resulting from these experiences. This evidence, found in the identification of problematic assumptions and the development of new tools and theories in the field, grounds our argument that standpoint theory is indeed relevant to the psy sciences and that many of those who have experienced marginalization and have engaged critically have an epistemic advantage when it comes to knowledge-production. The implications of this argument are significant: those who have attained a standpoint within the psy sciences ought to be included in research and given both tools and funding to develop research programs. However, we must be wary of the risks of tokenization, cooptation, and essentialization that are likely to accompany such a transformation.
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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 | 0.033 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.123 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".