Wisdom-of-the-crowd effect’s application in environmental impact assessment
Bibliographic record
Abstract
The wisdom-of-the-crowd effect is a counter-intuitive phenomenon that results in the reduction of errors under some strict conditions. Those conditions are related to randomness and large enough sample size. Many research fields examine the potential use of this effect in error reduction and in overcoming limitations such as the limited number of experts in a given field. Selected environmental/human health impact assessments have been qualitatively examined for the potential existence of this phenomenon. In addition, a quantitative evaluation has been performed for four case studies of simplified life-cycle assessment. The quantitative results indicate that for end-point damage categories, the geometric mean leads to more accurate results than the arithmetic mean. Results suggest that it might be possible to reduce errors or expand the application of these assessments by using assessors with less expertise. The wisdom-of-the-crowd effect also can be used to fill in data gaps in life-cycle inventory databases. However, further research is required in this area to explore the practical uses of this phenomenon. The proposed method can be applied to any entity to examine possible improvements in any area of environmental/human health research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".