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Record W4402170280 · doi:10.32920/26871469.v1

Inverting the Algorithmic Gaze: Confronting Platform Power Through Media Artworks

2024· preprint· en· W4402170280 on OpenAlexaff
Craig Fahner

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsToronto Metropolitan UniversityCentre for Social InnovationYork University
Fundersnot available
KeywordsGazePower (physics)Computer scienceHuman–computer interactionComputer graphics (images)Computer visionArtVisual artsArtificial intelligenceSociologyPhysics

Abstract

fetched live from OpenAlex

This dissertation looks to further the study of networked digital communication by centring material engagement and creative reconstitution as primary methods for the excavation of critical knowledge around platforms and algorithms. Platforms, which have become ubiquitous digital intermediaries in everyday life, are emblematic of the "control societies" described by Gilles Deleuze, in which electronic systems establish mechanisms of power and control that are both pervasive and inscrutable. Monopolistic digital platforms position the public in a highly asymmetrical relationship, in which users know very little about the processes by which algorithmic systems capture subjects through what I have termed "the algorithmic gaze": a process in which individuals are quantified and commodified according to the ever-expanding imperative of platforms to capture as much data as possible. The inscrutable nature of platforms and their algorithms demands innovative methods to unravel and analyse the cultural and political effects of platforms. This dissertation argues that conventional analyses of platform capitalism are strengthened through material encounters with algorithmic systems. By "opening up" platforms through creative reconstitution, new perspectives are gained on algorithms and their operation as political artefacts. Furthermore, I argue that the creation of artworks that reconstitute algorithmic technologies towards experiences that reveal, rather than conceal, the politics of platforms can serve as effective instruments for expanding critical literacy around digital technologies outside of a specialist context. Through the creation of a series of projects that confront the politics of platforms, this dissertation considers how media artworks might challenge existing imaginaries around platforms and algorithms, by engendering critical perspectives on platform capitalism and representing alternative models that resist the tendencies of the control society.

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)0.007
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0140.051
Scholarly communication0.0210.022
Open science0.0020.018
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0110.001

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 head0.042
GPT teacher head0.315
Teacher spread0.272 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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