Feminist Epistemologies of Situated Knowledges: Implications for Rhetorical Argumentation
Bibliographic record
Abstract
In the process of challenging epistemological assumptions that preclude relationships between knowers and the objects of knowing, feminist epistemologists Lorraine Code and Donna Haraway also can be interpreted as troubling forms of argumentation predicated on positivist-derived logic. Against the latter, Christopher Tindale promotes a rhetorical model of argument that appears able to better engage epistemologies of situated knowledges. I detail key features of the latter from Code, especially, and compare and contrast them with relevant parts of Tindale’s discussion of context on the rhetorical model, and I suggest ways that his work could be expanded to accommodate rhetorical implications of situated knowledges.
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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 | 0.032 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.105 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".