What a Difference a p(TA) Makes. Policy-Makers, Experts and the Public in Decision-Making on Risky Technologies
Bibliographic record
Abstract
It is almost a truism that technological innovation does not only hold great promises, but often involves substantial risks and even ethical problems. Thus, the question arises, who is going to decide on such ambiguous technologies in what way. The project CIT-PART studied comparatively the use and impact of participatory technology assessment (pTA) and expert-based technology assessment (TA) in science and technology policy in several EU Member States and organizations such as Austria (AT), Canada (CA), Denmark (DK), the European Commission (EC), Great Britain (GB), The Holy See (VA), Italy (IT), Latvia (LV), The Netherlands (NL), the OECD, Sweden (SE), and Switzerland (CH). (author's abstract)
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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 | 0.033 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.023 | 0.029 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".