Bibliographic record
Abstract
There was a little girl, she had a little curl, Right in the middle of her forehead; And when she was good, she was very, very good, And when she was bad, she was horrid. Henry Wadsworth Longfellow Competitive markets seem to have a great deal in common with the little girl who had a little curl. When they are good, they are so very good that our participation in them becomes part of our unconscious daily routine. If I want broccoli for supper, there is broccoli waiting for me at the grocery store. Down the aisle are the green peppers, locally grown in summer and Mexican in winter. The bananas are from Ecuador and the apples are from as far away as New Zealand. The presence of each item on the grocer's shelves is the result of a complex chain of decisions made by the grocer, the wholesaler, the shipper, and the farmer. Their actions are co-ordinated by prices, and this fact has important implications for the way in which specialized knowledge is utilized. The farmer does not need to know anything about shipping or the grocery business or the making of fertilizers, nor need he communicate to anyone his specialized knowledge of farming. He need know only the prices at which various crops can be sold, and the prices at which factors of production can be purchased.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.404 | 0.219 |
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".