Bibliographic record
Abstract
I am standing by the cheese cabinet in our local supermarket. (This is poetic licence, you understand. I am actually sitting at my PC, but my recent supermarket experience is so vivid that I have no difficulty reliving it.) I am in a state of high anxiety. In front of me are uncountable types of cheese. There is Canadian, Irish, Welsh, New Zealand and English. There is mild, mature, extra-mature, vintage and farmhouse. There is low fat, full fat—presumably ‘high fat’ would be a marketing disaster—and vegetarian. There are special cheeses in expensive waxy paper, or in the kind of customized black rind you see in Dutch markets. There is also ‘value’ cheese with a naff logo that allows you to proclaim your penury or your meanness. These are the varieties of Cheddar alone. Faced with such an obscene superfluity of Cheddars, how can I be certain of selecting the best value, the best taste, or exactly the one my wife will like? Being of a moderately obsessional turn of mind, I try to contain my anxiety by doing a mathematical calculation of how many different options there must be here: nationality times maturity, maturity times fat content, fat content times price band, and so on. The arithmetic soon falters. Firstly, it is clear that the grid may have gaps in it (the Welsh seem to make mature vegetarian Cheddar, but the Irish do not). I also start to have serious doubts as to whether I fully understand the taxonomy of Cheddar: is there such a thing as a mild farmhouse or an extra-mature non-vintage? I even begin to wonder if I shall need to invoke Venn diagrams or algorithms to sort the problem out.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".