Evoking Desire…and Irreverence: A Collection of Women Writing Women
Bibliographic record
Abstract
We draw from the diverse life experiences of women who have supported their academic and life journeys through membership in the Women Writing Women (WWW) collective. We come from diverse backgrounds in curriculum, new media studies, drama, english, art, science, creative writing, elementary, secondary, higher and adult education, and bring these multiple perspectives to our monthly dialogues. We explore how writing can evoke desire, longing, fear, reverence, irreverence, joy and awe rather than merely represent. The community offers an emergent space for these deeply personal, yet public explorations into meaning‐making. We share personal stories, perform writing, dialogue on the evolution of this collective, and co‐create with the audience gathered. Throughout the four years of conforming, unforming, reforming and transforming within this collective, we have come to understand that the simple and seemingly isolated act of personal and academic writing is a complex social reality. We articulate singularities in our writings and discussions as we simultaneously discover overlapping links within personal and collective metaphors. The paper opens a much‐needed dialogue on the complexity of transformational learning communities, particularly within academia. We hope to evoke dialogue and inspire among our readership to also create writing collectives as a form of ‘joyful revolt’ against isolating hegemonics, opening up a new space to explore collectivity and emergent possibility.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.026 | 0.016 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".