Bibliographic record
Abstract
In August 2018, there was a female orca who carried her dead calf to the surface of the Pacific Ocean for seventeen days. This kind of behavior had never before been documented. When the orca needed to feed or rest, her pod helped her carry the burden. During this time, the pod travelled more than 1,600 kilometres with the dead calf. Researchers say the orcas were in mourning and called this time their ‘Tour of Grief.’ Orcas are enormous mammals with the capacity to feel deep emotion. To Laura Quigley, this story speaks to the enormity of a mother’s heart and her fierce strength. It also speaks to the bond of the pod and their instinct to support one another, especially in the darkest moments. When Quigley gave birth to her second baby, she was struck by how isolated she felt. She was away from colleagues and many of her friends, and she lived far from her family because of her work. In speaking with so many mothers since writing this play, she understands that she was not alone in this feeling of segregation. As new mothers, we are without pods. The Waves dives into under-represented territory: loss of identity in motherhood, postpartum depression, and the rebuilding of self after giving birth. The text is poetic, raw, and urgent and explores ways the human voice can initiate understanding and connection between performer and audience, between humans. In this way, the text contains a detailed description of vocal sounds that depart from language, invoking the performer to explore relationship and communication beyond the conventions of speech and silence.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.245 | 0.130 |
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".