Bibliographic record
Abstract
My positioning focuses on the meaning of a single word, probability.As in the prior exercise, 1 my tasks are to (i) make explicit, to talk about, that which I am capable of talking about; (ii) to identify that which I am not capable of talking about; and (iii) to describe the view that results from developing what I take to be positional and reversing what I take to be counterpositional.Probability is a word that rings familiar and commonly occurs in daily questions and assertions."What is the probability that the baby will be a green-eyed girl?" "In all probability, we won't meet the deadline.""The probability of winning the lottery is quite low, but I will buy a ticket nonetheless."Like so many words, probability can be used intelligently and commonsensically without understanding distinct, uncommon meanings that are the fruit of doing apparently trifling problems in twofold-attention.I do not know how many of the authors who have published in the leading journals 2 in the last 50 years have appropriated the basic insights that are my focus in this essay.And the relevant researches, interpretations, and histories have not arrived in the mail, so I am not in a position to pick out some good things and some not-so-good things from those journals. 1 In "Effective Dialectical Analysis," I highlighted the importance of implementing diagrams and heuristics in my thinking, planning, and teaching.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".