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Record W2289331029 · doi:10.16997/wpcc.209

Digital Materialisms: Frameworks for Digital Media Studies

2015· article· en· W2289331029 on OpenAlexaff
Nathalie Casemajor

Bibliographic record

VenueWestminster Papers in Communication and Culture · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicCybernetics and Technology in Society
Canadian institutionsUniversité du Québec
Fundersnot available
KeywordsDigital mediaMedia studiesArtSociologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Since the 1980s, digital materialism has received increasing interest in the field of media studies. Materialism as a theoretical paradigm assumes that all things in the world are tied to physical processes and matter. Yet within digital media studies, the understanding of what should be the core object of a materialist analysis is debated. This paper proposes to untangle some of the principal theoretical propositions that compose the field of digital materialism. It outlines six frameworks that share the assumption that digital stuff is composed of material entities: the Berlin School of media, the field of software studies, the literary critique of electronic texts, the forensic approach, the ‘new materialist’ media ecology, and the field of Marxian critical studies. These different options are positioned along three main lines of tensions: between a semantic and an engineer’s perspective on media, between technological and social determinism, and between critical or post-humanist political propositions.

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)0.010
metaresearch head score (Gemma)0.009
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 score0.018
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.007
Science and technology studies0.0050.054
Scholarly communication0.0180.025
Open science0.0030.008
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0070.001

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 head0.050
GPT teacher head0.276
Teacher spread0.226 · 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

Citations56
Published2015
Admission routes1
Has abstractyes

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