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Record W7148988474 · doi:10.1386/jaac_00071_1

‘The women at Jessie’s have stories to tell’: A community creative writing programme for young mothers

2025· article· en· W7148988474 on OpenAlexafffundabout
Lauren Kirshner

Bibliographic record

VenueJournal of Arts & Communities · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicArtistic and Creative Research
Canadian institutionsToronto Metropolitan University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSisterAgency (philosophy)NarrativeCreative writingNarrative inquiryIdentity (music)

Abstract

fetched live from OpenAlex

This case study examines Sister Writes at Jessie’s, a creative writing programme for young mothers in Toronto, developed with Jessie’s: The June Callwood Centre for Young Women and embedded in the long-running Sister Writes programme. Informed by arts-based research (ABR) and critical arts-based inquiry (CABI), the initiative supported participants in asserting narrative authority through writing, publication and community-engaged public presentation. Working with acclaimed women writers, participants wrote and revised personal stories that culminated in a professionally produced magazine, a mini documentary voiced by participants and a public launch at a community cultural venue. By pairing production with public circulation, the programme positioned participants as authors and cultural contributors with expertise rooted in lived experience. This case study demonstrates how community creative writing programmes can function as narrative interventions, fostering authorship, agency and recognition for young mothers while producing lasting forms of knowledge.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.008
Scholarly communication0.0030.003
Open science0.0020.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.105
GPT teacher head0.352
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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