Empowering the Female Voice: Interdisciplinarity, Feminism, and the Memoir
Bibliographic record
Abstract
Women’s voices, historically, have been silenced or Othered, but, through the interdisciplinary genre of the memoir, women are able to write from a female perspective and create a strong voice for feminism. By sharing the reality of the female experience, the memoirist ultimately reveals truths about her own life and, in doing so, examines the world in which she lives—especially with regard to gendered identity and social norms. This paper explores feminist and interdisciplinary aspects of the contemporary female-authored memoir. I focus on two key feminist issues—women’s role or gendered identity and the male gaze—within the memoirs of bell hooks, Caitlin Moran, and Tina Fey, incorporating interdisciplinary and feminist perspectives. Through their personal narratives, Moran, hooks, and Fey, explore politically charged topics (that are often silenced due to social stigma), while voicing their feeling of dissent toward social and gendered norms; ultimately, generating and promoting important feminist discourse.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.029 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".