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Record W2586304006 · doi:10.1177/1362480617690800

Of “old” and “new” ways: Generations, border control and the temporality of security

2017· article· en· W2586304006 on OpenAlexafffundabout
Karine Côté-Boucher

Bibliographic record

VenueTheoretical Criminology · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTemporalitiesTemporalitySociologyScholarshipNegotiationCentralityValue (mathematics)Anticipation (artificial intelligence)SocializationControl (management)EpistemologyPolitical scienceSocial scienceLawManagement

Abstract

fetched live from OpenAlex

Whether it insists on the significance of anticipation or interrogates the centrality of pre-crime to security practice, current scholarship misses how security professionals make sense of their work’s temporality. Borrowing its theoretical tools from the sociology of generations and evaluation, this article focuses on how Canadian border officers rely on generational categorizations to negotiate change in their work. It proposes exploring the coexistence of competing temporalities in border control through the notion of generational borderwork. Produced by different paths of professional socialization and embedded in tensions over social status in ports of entry, generational borderwork makes more explicit the security field’s logic of aging, the internal dissensions over policing methods and the decisions these differences sustain. Whether it concerns nostalgia for economic protectionism or disagreements over the respective value of intelligence, technologies and interview skills, the contested nature of time in border control invites investigation into officers’ transforming policing sensibilities.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score0.337

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.042
Scholarly communication0.0090.008
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.120
GPT teacher head0.407
Teacher spread0.287 · 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 designQualitative
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

Citations20
Published2017
Admission routes3
Has abstractyes

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