Bibliographic record
Abstract
Sister Writes is a creative writing and literacy program dedicated to honouring the wisdom and experiences of women in downtown Toronto. Founded by writer Lauren Kirshner in 2010, with the support of Sistering, a drop-in center empowering ordinary women in extraordinary situations, Sister Writes provides women with the opportunity to work with professional women writers, develop creative potential, hone literary and leadership skills, receive mentorship, and build self-esteem. Hailed by The Toronto Star as a groundbreaking program, and the winner of a 2018 ArtsBridges Award for community arts education excellence, Sister Writes is one-of-a-kind in Canada. Now in its twelfth year, Sister Writes provides an artistically empowering and collaborative creative writing program for women affected by poverty, underhousing, precarity, trauma, mental health issues and addictions, and women who face extraordinary circumstances or life transitions. Through the support of our sponsors and the assistance of our guest writers, Sister Writes offers a range of hands-on programming that is free and inclusive: The Writing Workshop; The Mentorship Program; and our newly launched Sister Writes in the Community Program, which brings one-off creative writing workshops to women’s agencies across Toronto. Since 2010, our longest running program, The Writing Workshop, has provided over 300 free creative writing workshops in the community for budding women writers, and published nine literary magazines. Our guest workshop leaders have included acclaimed authors, poets, and journalists. Empowering, inclusive, and hands on, Sister Writes is dedicated to the principle of breaking down barriers to arts, one story at a time.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.599 | 0.349 |
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".