expert: Modeling Without Data Using Expert Opinion
Bibliographic record
Abstract
package provides tools to create and ma-nipulate empirical statistical models using expertopinion (or judgment). Here, the latter expressionrefers to a specific body of techniques to elicit the dis-tribution of a random variable when data is scarce orunavailable. Opinions on the quantiles of the distri-bution are sought from experts in the field and aggre-gated into a final estimate. The package supports ag-gregation by means of the Cooke, Mendel–Sheridanand predefined weights models.We do not mean to give a complete introductionto the theory and practice of expert opinion elicita-tion in this paper. However, for the sake of complete-ness and to assist the casual reader, the next sectionsummarizes the main ideas and concepts.It should be noted that we are only interested,here, in the mathematical techniques of expert opin-ion aggregation. Obtaining the opinion from the ex-perts is an entirely different task; seeKadane andWolfson(1998);Kadane and Winkler(1988);Tver-sky and Kahneman(1974) for more information.Moreover, we do not discuss behavioral models (seeOuchi,2004, for an exhaustive review) nor the prob-lems of expert selection, design and conducting of in-terviews. We refer the interested reader toO’Haganet al.(2006) andCooke(1991) for details. Althoughit is extremely important to carefully examine theseconsiderations if expert opinion is to be useful, weassume that these questions have been solved previ-ously. The package takes the opinion of experts as aninput that we take here as available.The other main section presents the features ofversion 1.0-0 of package
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.015 |
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".