Bibliographic record
Abstract
On Data, Media, and the Deconstruction of the Administrative StateI shall start with some belated and limited conclusions.1 In elections in the United Kingdom and the United States in 2016, an unstable bloc of militant liberals and neo-fascists fashioned new media and data practices to smash existing political norms and institutions in order to restructure reality, 'deconstruct the administrative state', and further hollow out democracy and de-regulate capital. 2 Ongoing press, parliamentary, congressional, and legal investigations on both sides of the Atlantic broadly reveal that political actors re-worked the recently established practices of 'surveillance capitalism' to marry the data produced by people in their interactions with social media and the Internet to 'psychographic messaging' designed to influence their thoughts and actions.3 Commercial procedures of data surveillance such as those integral to the business model of entities like credit-rating agency Experian (1996) and multinational search and social media corporations like Google (1999) and Facebook (2004) were supplemented by governmental practices of mass data surveillance such as the PRISM programme from 2007 as part of the expansion of 'exceptional' state practices in the ongoing 'War on Terror'. 4 In the early 2000s, hybrid governmental/ commercial consulting institutions began meshing data surveillance with what one of the British entities close to the centre of this history -Strategic Communication Laboratories (SCL)called 'influence operations'.5 SCL deployed this data/media complex in elections and referendum campaigns across the world, including Australia, Brazil, Gambia, Ghana, Indonesia, Kenya, Nigeria, Philippines, Thailand, and elsewhere.6 Cambridge Analytica, much in the news across 2018, grew out of SCL and built on data harvested from the data Facebook sells access to (and public data sets such as censuses, credit reports, insurance data, and so on) to construct psychological profiles of populations for political campaigns in the United States and the United Kingdom to develop a near-personalized propaganda system using digital screen media to influence political attitudes and conduct.7
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.043 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".