Bibliographic record
Abstract
My quest for necessary wisdom about mothering, teaching, researching—coalescing here as art and research—comes by way of a mantra enacted between mother and daughter onto the world: milk, heat, time. An awareness of mantra as methodologically potent occurred through the artistic practice of drawing with mother’s milk, heat, and time. Traveling through the twists and overlaps of my complicated existence as a mother, artist, teacher, and researcher this paper offers three imaginable potencies for arts-based research: mantra unfolds myth, mantra intensifies listening, and mantra generates reciprocity. This contemplatively and performatively crafted text brings to light these three potencies through multiple modes of data-creation: mantra, time, drawing, sensation, song, memory, connection, affect, photographs, writing, and materiality. Linger with me in the place where unconsumed mother’s milk—fat and water separating in plastic sleeves and bound for the trash in a university childcare setting— coalesced with cultural myths of breastfeeding, embodied memories of labor and returning to work after childbirth, grassy-scented infant’s breath, lullabies, milk scorching on a hot iron, cotton clothing, silver salts embedded in light-sensitive paper, and more. Linger and listen—water, bodies, stories, hidden, unhidden.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".