Technoliberalism in Iceland: The Fog of Information Infrastructure
Bibliographic record
Abstract
Background In the wake of the 2007–2008 financial crisis in Iceland, some citizens believed the calamity was the outcome of a cultural of secrecy among the political and financial elites.Analysis By examining an effort to legislate for a “data haven” in Iceland, this article discusses a shift in how data activists attempted to achieve data justice. This shift challenges existing ideas about cyberlibertarian and technoliberal approaches to social change. In attempting to address the inequalities inherent to the centralization of data and the internet, data activists moved away from advocacy and adopted two previously rejected strategies: formal political organizing and territorial authority.Conclusion and implications Activism for data equity was insufficient to counter existing data power in Iceland. What comes after technoliberalism?Contexte Suivant la crise financière de 2007-2008 en Islande, certains citoyens se mirent à penser que ce désastre était le résultat d’une culture du secret parmi les élites politiques et financières du pays.Analyse Par l’examen d’efforts pour légiférer un « havre de données » en Islande, cet article discute d’un changement dans la manière dont des militants ont tenté d’établir un accès plus juste aux données. Ce changement pose un défi à des idées courantes prônant une approche cyberlibertaire et technolibérale envers le changement social. Les militants, en tentant de s’adresser aux inégalités inhérentes à la centralisation des données et d’internet, se sont éloignés du plaidoyer pour adopter deux stratégies rejetées antérieurement : l’organisation politique formelle et l’autorité territoriale.Conclusion et implications Le militantisme pour l’égalité des données s’est avéré insuffisant pour démocratiser le contrôle des données en Islande. Dans ces circonstances, qu’est-ce qui pourrait suivre au technolibéralisme?
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".