Bibliographic record
Abstract
Women’s voices have historically been silenced in a vast array of contexts. Ethical incongruities exist between theoretical perspectives regarding right action for protection of women’s dignity and the tangible dilemma presented by systemic silencing. A fictive imagination found in the arts – and literature in particular – often plays a role in bridging that ethical gap between theory and practice. Using my arts-based approach of poet(h)ic inquiry (Duff, 2016a), I portray the symbolic power of women’s voices, fictionality, and textual polyvocality in a research-based play. Poet(h)ic inquiry is a method for ethical reflection incorporating spiritual and poetic-aesthetic values: a pedagogical space of inquiry within a non-fixed site of teaching, life-long learning, creativity, and knowing, located at the confluence of the creative writing process (in the context of fiction as research), ethics, and spirit. In “Story about Story. Toronto 2001,” I inquire poet(h)ically, in a speculative fictional tale about a woman’s journey with her baby, using research journal data and “freefall writing” notes as springboard for a “fictive leap” (Mitchell, 1977). Through the fictive writing process, knowledge is generated with respect to themes of isolation and connection towards re-finding the lost self’s language. Voices heard and unheard, pinpoint an ethic of meaning towards transcending silence, suffering, and colonial injustices. My story evokes ironies and eco-ethical queries within wildlife research, as well as questions evoked by the sensory overload of urban commerce, and an unspoken class system. I include reflections on fictionality, literature, and redemption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.069 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".