Bibliographic record
Abstract
BECAUSE I DID NOT SEE By Deirdre Hennings On sait connu les Cafe Des Trois Colombes au rendezvous des amours sans abris… We know the Cafe of the Three Doves The rendezvous of lovers without refuge … Joe Dassin’s lyrical melody washes over me recalling our joyous summer in Montreal, compelling my limbs to lift and linger in the evening air timeless again dancing for him as I used to warm and sweet, sensual and smiling. We kiss tenderly before I paddle off to make toast. But before I sense anything he barks that it’s burning. I pop up the bread— untoasted, I see nothing amiss—but it’s too late. His over-chemo’d body slumps as he holds his stomach, pain clouding his face. “Why do you always do this?” he cries. But I did nothing except fail to notice an errant drip of soup waiting on the toaster top to burn. Now he is nauseous again and the evening is ruined because I did not anticipate because I did not see.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.089 | 0.036 |
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".