Bibliographic record
Abstract
"Culture is more than food and folk music," charged one participant of a recent conference celebrating Canadian writers of Italian descent) The comment was made over a late lunch and, since most of us were munching bread, one eye fixed firmly if discretely on the next mouthful - bread in our left hands, butter knives in our right - we nodded rather than voiced our assent. We all agreed. How could we do otherwise? Not only would dissent have been a rude slight to the central assumption of the conference - that "culture" was the soul and not the surface of a community - but we were all, at that very moment, participating in a ritual that proved his point. We were not just eating bread, but breaking it together in a ritual of community building. Besides, it would have been awkward had we spoken with our mouths too full and more awk- ward still had we not responded at all. So, together, we negotiated the fine balance required by such an occasion: the bread course of a lunch shared between approximately twelve scholars, most of them Canadians of Italian descent. Conversation, by the way, was English, spiced with the occasional Italian phrase. Given that the lunch was held in Montreal, it might have been a French language affair. That it was not was an unspoken but not insignificant negotiation.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.049 | 0.043 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".