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Record W4407332788

Manifest Spatialization: Militarizing Communication in Canada

2015· article· en· W4407332788 on OpenAlexaffabout
Patricia Mazepa

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsYork University
Fundersnot available
KeywordsSpatializationGeographyComputer scienceArtLiterature
DOInot available

Abstract

fetched live from OpenAlex

Focusing on the political economy of communication and the process of spatialization whereby control over space and time is extended through the use of information and communication technology (ICT), this paper provides an overview of the intersections that draw the Canadian federal government, its military, and the ICT, defence and security industries into relationships that reinforce and extend their control. By attending to historical and current examples, it highlights several sub-processes of spatialization, including corporate restructuring (through vertical and horizontal integration), as well as state restructuring (principally through internationalization and commercialization), which together underpin and support the militarization of communication. From the state’s concentration on conventional war and the “Cold War”, through to the current “War on Terror” and its protection of an integrated ICT infrastructure, communication is increasingly confined within a narrow militarized and corporatized framework. Within this framework, both capital and the military prioritize the development and administration of the “command and control” capabilities of ICT, such that the policies and practices of communication become more exclusive, restrictive and surveilled, and less open, accessible, and universal. The paper seeks to explain how this tripartite combination of “command, control, and communication” is indicative of the process of spatialization, and supports a militarized capitalism and the formation of a cross-border MICC poised to expand and defend it.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.395
GPT teacher head0.566
Teacher spread0.171 · 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 teacher head, not a consensus.

Study designObservational
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

Citations2
Published2015
Admission routes2
Has abstractyes

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