MétaCan
Menu
Back to cohort
Record W7135004341

Nothing to See Here: Generative AI, Neoliberal Crisis, and Intensified Counterinsurgency

2025· other· en· W7135004341 on OpenAlexfundno aff
Grayson Richards

Bibliographic record

VenueYorkSpace (York University) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersUniversity of OxfordYork UniversityUniversity of Minnesota
KeywordsDisinformationGovernmentalityNeoliberalism (international relations)NothingPoliticsOperationalizationGenerative grammarNarrativeField (mathematics)Dystopia
DOInot available

Abstract

fetched live from OpenAlex

Examining generative artificial intelligence (genAI) as a discursive and technical agent deployed in response to the ongoing crisis of neoliberal legitimacy, this dissertation advances the claim that so-called “safe” models extend the counterinsurgent (COIN) mode of governance, both as an effect of the technology’s inherent mode of visualizing “meaning” from unstructured data, and through their operationalization as instruments of epistemic and political management. Anchored by Mirzoeff’s genealogy of Visuality—defined as the ‘visualization’ of the social in ways that separate subjects and authorize centralized control—the dissertation’s narrative is organized into three parts. The first develops a historico-genealogical account of media, visuality, and power, showing how media technologies reflect and intensify particular visualities, culminating in the emergence of algorithms as the media form native to a mode of visualization which ‘sees’ the social as a heterogeneous field to be stabilized through counterinsurgency. Part two narrates the current intensification as an establishment response to the deepening crisis of neoliberal governmentality and the runaway aggregation of insurgency enabled by platform algorithms, securitizing genAI through appeals to epistemic integrity, AI safety, and existential risk in authorization of extrapolitical information controls. Finally, part three traces this intensification of COIN through an analysis of the complementary regulations, technical countermeasures and epistemic interventions constructing the “safe” model as a palliative to the “existential threats” of AI-enhanced disinformation and its corollary: infocalypse. GenAI emerges at a conjuncture where digital networks have made it increasingly difficult to sustain stability via COIN alone. This dissertation contends that, absent meaningful political economic reformation, authority has instead resorted to progressively heavy-handed interventions in the digital public arena, birthing a complex of protocols which in effect produce “safe” generativity as a means of policing epistemic and political legitimacy through the tactical disaggregation of mounting insurgency. Recognizing that, while an extension of COIN visuality, genAI simultaneously reveals this intensification as the product of an ultimately mutable system, the dissertation concludes with a speculation on the possibility of appropriating generativity to visualize realities emancipated from the oppressive legacies of Visuality.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0050.056
Scholarly communication0.0150.014
Open science0.0010.008
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.013
GPT teacher head0.225
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueYorkSpace (York University)French-language works237,207