Bibliographic record
Abstract
Last September Infinitheatre, my small Montreal “risk theatre,” took a bilingual production of Beckett’s Fin de Partie/Endgame to the thirteenth Cairo International Festival for Experimental Theatre. We had originated our production in Old Montreal, in the unheated shell of a former foundry where temperatures dipped to freezing in the November chill. With the breath of the actors visible in the air, the audience huddled together under blankets to watch the love/hate power struggle of Hamm and Clov. Hamm was played by a Francophone (Jean Archambault) and Clov by an Anglophone (Sean Devine); this allowed us to graft together Beckett’s own French and English versions. Language became part of the battleground as the characters switched between French and English in mid-dialogue with the frequency and ease we Montrealers do in our daily existence. Clov’s constant threat: “Je te quitte,” the mantra of anglo Quebec, took on a very specific resonance. Somehow word of our production reached the Egyptian Consul in Montreal and we were invited to participate in what Egyptians consider one of the great international cultural events of the Arab world.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.003 |
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".