Critical Thinking, Bias and Feminist Philosophy: Building a Better Framework through Collaboration
Bibliographic record
Abstract
In the late 20th century theorists within the radical feminist tradition such as Haraway (1988) highlighted the impossibility of separating knowledge from knowers, grounding firmly the idea that embodied bias can and does make its way into argument. Along a similar vein, Moulton (1983) exposed a gendered theme within critical thinking that casts the feminine as toxic ‘unreason’ and the ideal knower as distinctly masculine; framing critical thinking as a method of masculine knowers fighting off feminine ‘unreason’. Theorists such as Burrow (2010) have picked up upon this tradition, exploring the ways in which this theme of overly masculine, or ‘adversarial’, argumentation is both unnecessary and serves as an ineffective base for obtaining truth. Rooney (2010) further highlighted how this unnecessarily gendered context results in argumentative double binds for women, undermining their authority and stifling much-needed diversity within philosophy as a discipline.These are damning charges that warrant a response within critical thinking frameworks. We suggest that the broader critical thinking literature, primarily that found within contexts of critical pedagogy and dispositional schools, can and should be harnessed within the critical thinking literature to bridge the gap between classical and feminist thinkers. We highlight several methods by which philosophy can retain the functionality of critical thinking while mitigating the obstacles presented by feminist critics and highlight how the adoption of such methods not only improves critical thinking, but is also beneficial to philosophy, philosophers and feminists alike.
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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 | 0.093 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.017 | 0.112 |
| Scholarly communication | 0.034 | 0.069 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".