Bibliographic record
Abstract
We are members of an Ottawa-based climate humanities group focused on developing new feminist methods and practices of co-writing that unsettle the liberal self in order to build a more sustainable future. In this reflection, we focus on one of these methods: audio recordings. After using a cellphone to record one of our undisciplined conversations on co-writing, care, and climate justice, we individually listened back to the conversation and reflected on both the content of our conversation and our feelings and thoughts related to the act of listening back. We ask what impact the practice of recording our conversation has on us as climate scholars and activists. How do we listen to each other in person? How does our listening change when we play back the recording, especially in regard to how we orient ourselves to others? We also suggest a capacious definition of co-writing that follows from the recording method itself: our bodies in the room together; the words, “mmms,” and “yeahs” being spoken, heard, and then heard again later and differently; and perhaps most nefariously, the cellphone and all of its material relations, tucked under a plate of cookies on the table, recording everything we say. Taken together, our new method of listening and our extended definition of cowriting seek to create a strategy for actualizing feminist climate futures today.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".