Bibliographic record
Abstract
Years ago I published a book called Blooded Thought, the title of which came out of a line by the poet Wallace Stevens: ‘‘An abstraction blooded, as a man by thought.’’1 Actually, some of that blood rushed to the head when I was first directing Beckett’s plays – in San Francisco, more than half a century ago – and would often speak to the actors in a Beckettian way, my words inseparable from his words, as a way of thinking through or by means of what it is that he thought. I’ve written several essays in the past in which I thought thus, and there are passages here in which I’ll be doing the same. If, then, I sometimes indicate quotations and sometimes (or mostly)don’t – though I think you’ll know when he’s speaking – it’s because I’m trying to convey, in the moment, what he thought, as I think it. I should say that one of the things confirmed by Beckett every time I read him is that it’s precisely where thought escapes me, at the selvage or circuitous limit of thought, that it may, tautologically, turn back upon itself, which is what keeps it going, to be thought all over again – this time, to begin with, in even more personal terms.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".