Weighting and aggregating expert ecological judgments
Bibliographic record
Abstract
Performance weighted aggregation of expert judgments, using calibration questions, has been advocated to improve pooled quantitative judgments for ecological questions. However, there is little discussion or practical advice in the ecological literature regarding the application, advantages or challenges of performance weighting. In this paper we (1) illustrate how the IDEA protocol with four-step question format can be extended to include performance weighted aggregation from the Classical Model, and (2) explore the extent to which this extension improves pooled judgments for a range of performance measures. Our case study demonstrates that performance weights can improve judgments derived from the IDEA protocol with four-step question format. However, there is no a-priori guarantee of improvement. We conclude that the merits of the method lie in demonstrating that the final aggregation of judgments provides the best representation of uncertainty (i.e., validation), whether that be via equally weighted or performance weighted aggregation. Whether the time and effort entailed in performance weights can be justified is a matter for decision-makers. Our case study outlines the rationale, challenges, and benefits of performance weighted aggregations. It will help to inform decisions about the deployment of performance weighting and avoid common pitfalls in its application.
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 | 0.219 | 0.444 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".