Bibliographic record
Abstract
‘Leo’ is an excerpt from my memoir in progress, Talk Recovery. The manuscript tells the story of my recovery from alcoholism in Alcoholics Anonymous (AA) in Puerto Vallarta, Mexico, where I moved after living in Canada for ten years. To narrate my manuscript in a way that suggests the oral AA ‘share,’ I adapt the ‘talk poem’ form invented by the US performance poet David Antin (1932–2016). An AA share occurs when AA members speak in an improvised way about their experiences with alcoholism recovery in an AA meeting. Antin’s talk poems were also improvised verbal performances that he recorded and then transcribed and edited for publication. Antin’s published talk poems employed a number of typographical features to denote speech on the page that I have adapted for my memoir, including white space to indicate pauses. My narration also contains specific AA share characteristics, such as set phrases that speakers commonly make use of and that represent AA principles as well as a shared culture. AA is a controversial program, with both critics and supporters. ‘Leo’ presents some of these debates in a dramatized way, as a discussion between the two characters.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.161 | 0.073 |
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".