Bibliographic record
Abstract
When I was a boy my parents smoked—as did most adults who I knew. People lived and then they died. I lived in a council house and went to a comprehensive school. I ate pies, cakes, and pastries, all washed down with full fat milk. When we could afford it we ate butter, but most of the time it was Stork Special Blend margarine. We had free school dinners and I loved spam fritters, sausage pie, stew, corned beef, or meat loaf, followed by pink custard and sponge. I loved our dinner ladies, and I often had third helpings. All my spare money went on Cabana bars, crisps, and a “quarter” of boiled sweets. My first McDonald's burger sent me delirious with its flavour. > I found that my nagging was patronising and pointless Regardless of the weather, we fought and played …
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.014 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.094 | 0.019 |
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".