Managing Public Expectations of Technological Systems: A Case Study of a Problematic Government Project
Bibliographic record
Abstract
In this discussion piece we address how the UK government has attempted to manage public expectations of a proposed biometric identity scheme by focussing attention on the handheld, i.e., the ID card. We suggest that this strategy of expectations management seeks to downplay the complexity and uncertainty surrounding this high-technological initiative, necessitating the selective use of expertise for the purpose of furthering government objectives. In this process, government often relegates counterexpertise, if not dismissing it outright, thereby greatly politicizing the policy deliberation process. We argue that this manoeuvring by government spells trouble for both democratic deliberation on the issue of biometric identification in the UK and, more generally, expertise-based policy making in related technological ventures.
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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 | 0.022 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.017 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".