Speak-to-Write from Multiple Perspectives, as Method
Bibliographic record
Abstract
One of the practice-based research methods that excites me the most today is to work with writing as a practice, and as a practice-based research method. The technology of writing can be very misleading, especially when that writing is typed using a word processing program. When using this tool, writing looks the same whether it represents a stream of consciousness, a first draft, or a final proof. Because of this, I have found that I hold myself to the standard of the final version, which of course completely freezes me up. If we are always aiming for the final version, there is not much room for thinking, making errors, going sideways and backward and forward again. There is only the guaranteed feeling of failure. In response, graphic designer Juliette Bellocq and I have developed a set of writing exercises that address these two limitations, as I have come to know writing from my training as an academic. In this piece, we share our exercise, 1,000 Ways Home. It is a non-linear process of thinking and writing. It also offers the alchemy of communicating in the presence of another person who pays close attention. We call our process speak-to-write.
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 | 0.038 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".