Illusions of perfect information and fantasies of control in the information society
Bibliographic record
Abstract
This article introduces the idea of ‘risk societies’ to highlight how conventional views of the information economy are confounded by the productivity paradox, uncertain demand for new information and communication technologies (ICTs), and the heterogenous qualities of information. Confronting these realities, the communication industries are using monopolization strategies, surveillance, and technological design in their, often elusive, attempts to manage risk and turn the scarce resources of the media economy - time, money and attention - into economic value. These strategies erode the ‘soft factors’ of trust, confidence, social networks and privacy that are vital to people’s willingness to embrace new ICTs and the legitimacy of the information society. Although these trends have created space for new privacy enhancing technologies and trust-brokers, the translation of sociocultural norms into technology and market-based solutions renders communicative spaces more opaque than ever.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.087 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".