Bibliographic record
Abstract
Watch Voir le jour from Natalie Doonan on Vimeo This research-creation project focuses on breastfeeding in public as an act of claiming space for the common good. Its audio-visual component, “Voir le jour,” is part of a larger work that includes community screenings and locative storytelling. “Voir le jour” consists of recordings of nursing mothers, breastfeeding experts, and activists sharing their stories about the joys and challenges of breastfeeding outside the home. These stories are accompanied by a slideshow of photographs depicting moms nursing in public spaces. “Voir le jour” renders the labour of mothering audible and visible in public spaces, including online. It was created in part for use by the breastfeeding support organization Nourri-Source Montréal. “Voir le jour” contests the notion that “public” and “private” are distinct spatial categories. The fact that breastfeeding is commonly relegated to the sphere of domestic activity is a testament to the tenacious grip of patriarchy in everyday life, and leads to isolation for many new mothers. The discomfort that may be felt in response to seeing intimate experiences between mothers and babies is part of transforming the myth that private and public activities and spaces are discrete and separate spheres.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".