Can femme be theory? Exploring the epistemological and methodological possibilities of femme
Bibliographic record
Abstract
Narrative-works are the lifeblood of femme scholarship. Through this medium, femmes write themselves into existence. In this article, I begin with my own story of femme and examine the backdrop of patriarchal femininity that positions pieces of me as being at odds, disjointed, and something needing to be reconciled. Indeed, many current frameworks and dominant framings for understanding femininity create disjunctures needing to be reconciled and fail to include diverse feminine perspectives in ways that constitute epistemic and hermeneutical injustices. Using my own femme becoming as a guide, I offer this process of femme reconcilement as a framework that can be applied to dislodge feminine normativity and challenge the assumptions researchers make about femininity within their work. In this article I highlight the importance of femme epistemologies; the importance of valuing feminine knowledge, and how the absented femme highlights the continued god-trick of objectivity. Here, I discuss how femme narratives can be used to bolster femme as theory and critical analytic. This situated knowledge holds the possibility to inform novel methodological frameworks and to substantially shift the way researchers think about femininity and feminine people.
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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 | 0.037 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.104 |
| Scholarly communication | 0.020 | 0.032 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".