Understanding the Role of Citizens in Regulating the Surveillance State of the 21st Century
Bibliographic record
Abstract
Dr. David Murakami Wood, Associate Professor, Sociology, and Canada Research Director, Surveillance Studies, Queen’s University, Kingston, Ontario, Canada. This workshop explores the question of global surveillance, information and everyday life in the world that has been revealed by a whole range of contemporary phenomena from the Edward Snowden revelations to the theft and public posting of private photos. It identifies three connected trends. The first is the ‘opening up’ of both the surveillance apparatus and the lives of individuals, with closed networks of surveillance being connected to the public Internet and private data stored in ‘the cloud’ and shared both willingly and otherwise. The second is the ‘crowdsourcing’ of social and organizational practices of surveillance over these networks. The third is the gradual ‘infrastructurization’ of surveillance as these new surveillance networks are embedded in our lives through ‘smart’ objects, homes, cities and so on. Almost all states and corporate actors are taking advantage of the massively increased availability of data and the possibilities of greater knowledge and control, but at the same time, this new openness is also posed as a threat with attempts to associate openness with threats to decency, law and democracy, and ultimately, with ‘cybercrime’ and terrorism. The talk concludes that the new world of what I call ‘ambient government’ will not necessarily equate to the kind of more democratic ‘transparent society’ hoped for by some advocates, nor will it be (only) a technological authoritarianism, rather it will involve a more complex, contradictory and messy reconfiguration of social life, but one in which surveillance will remain central and essential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.075 |
| Scholarly communication | 0.024 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".