Bibliographic record
Abstract
Bring out number, weight and measure in a year of dearth. William Blake, Proverbs of Hell Probability distributions We are now in the following situation. We have a probability mass of unity, and we wish to distribute this over the possible outcomes of events and quantities of interest. We also wish to manipulate the results to assist in decision-making. We have so far considered mainly random events, which may take the value 0 or 1. A common idealized example that we have given is that of red and pink balls in an urn, which we have labelled 1 and 0, respectively. Thus, the statement E = ‘the ball drawn from the urn is red’ becomes E = 0 or E = 1, depending on whether we draw a pink or red ball. The event E either happens or it does not, and there are only two possible outcomes. Random quantities can have more than two outcomes. For the case of drawings from an urn, the random quantity could be the number of red balls in ten (or any other number) of drawings, for example from Raiffa's urns of Figure 2.10. A further example is the throwing of a die which can result in six possible outcomes; the drawing of a card from a deck can result in 52 possible outcomes, and so on.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".