Bibliographic record
Abstract
Arbitrary state and corporate powers are helping to turn the Internet into a global surveillance dragnet. Responses to this novel form of power have been tepid and ineffective. Liberal critiques of surveillance are constrained by their focus on privacy, security and the underlying presupposition that freedom consists only of freedom from interference. By contrast, (post)Foucauldian critiques rejecting liberalism have been well rewarded analytically, but have proven incapable of addressing normative questions regarding the relationship between surveillance and freedom. Quite apart from these debates, neorepublicans have excavated a third concept of freedom, understood as non-domination. Could neorepublicanism overcome the limitations of liberal and (post)Foucauldian critiques of surveillance? We argue, positively, that neorepublicanism can accommodate much of the (post)Foucauldian analyses while also incorporating a normative critique of surveillance vis-à-vis freedom. We further argue, negatively, that surveillance power has outstripped the capacities of traditional republican institutional responses to domination. We conclude by considering ways in which neorepublicanism can be recalibrated to address the novelty of surveillance power while adhering to the ideal of non-domination. Two ways of addressing the problem are proposed: an offensive, dedicated surveillance antipower and a defensive republican amplification of privacy.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".