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Record W2315938711 · doi:10.3167/trans.2016.060107

Target Practice

2016· article· en· W2315938711 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueTransfers · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsGovernmentalityRace (biology)BiopowerIntersection (aeronautics)SociologyDemocracyPolitical scienceLaw and economicsGender studiesPoliticsLawGeographyCartography

Abstract

fetched live from OpenAlex

Taking the Canada–U.S. border as a starting point to refl ect on emergent smart border practices, this essay analyzes the diff erential yet central place that race continues to hold in the regulation of mobilities through the technopolitical mechanism of the border. Against claims that smart borders off er a more scientifi c and “postracial” mode of border control, the essay off ers a situated conceptual refl ection on how race is currently being (re)shaped by the complex intersection of biopolitical and algorithmic forms of governmentality as they converge in border technologies. Th e essay proposes to think through four diff erent sets of smart border technologies that enact and track race as a biopolitical assemblage in particular ways, analyzing the associated perceptual codes each puts into play (biometric, movement sensing, drone, and databased). It closes by refl ecting on how these algorithmic technologies infl ect the biopolitical targeting of race and mobility in ways that serve to insulate smart border practices from democratic accoun tability.

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.

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)0.000
metaresearch head score (Gemma)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.026
GPT teacher head0.336
Teacher spread0.310 · 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