How to clean a fish and other adventures in Portugal
Bibliographic record
Abstract
"How To Clean a Fish is an inviting family travel story about an extended stay in Portugal, full of food and cooking adventures, language barriers and bureaucracy, and that irresistible need to connect with the culture of our birth. After immigrating to Canada as a young child, Esmeralda Cabral remembers the initial shock--the weather, the wide and empty streets--and the immediate longing, saudade, to return to her homeland. That longing changed over time but never completely left. Cabral and her Canadian-born family had visited Portugal as tourists, but were now returning as residents for eight months, bringing along their Portuguese Water Dog, Maggie. Cabral has a deep desire to pass on her heritage to her children. By exploring the intricacies of adapting to a culture that is at once familiar and foreign, she reveals that the search for identity and belonging is a universal story. How to Clean a Fish will appeal to travellers and foodies, those curious about Portuguese culture, and to anyone who has moved from one place to another and is searching for their own version of 'home.'"--
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".