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Record W2585788453 · doi:10.20381/ruor-19248

Nouvelle vague: The securitization of the US-Canada border in American political discourse

2009· dissertation· en· W2585788453 on OpenAlexaboutno aff
Geneviève Piché

Bibliographic record

VenueuO Research (University of Ottawa) · 2009
Typedissertation
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsSecuritizationPoliticsPolitical scienceBusinessLawFinancial system

Abstract

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In recent history, particularly over the last decade, the US-Canada border has been increasingly treated as a security issue. During this period, policies and measures have been put in place, such as strict identification documentation requirements, advanced surveillance equipment, information-sharing between law-enforcement and intelligence agencies on both sides of the border, and greater numbers of border patrol agents. These measures represent a significant departure from what was previously understood as a permeable, "undefended" border that prioritized above all else the facilitation of trade and travel. In my study, I have sought to better understand the process by which the US-Canada border is becoming understood by some as a security issue. Participants in critical security studies argue that issues, such as borders, become security matters through a process. The Copenhagen School (CoS) argues that this process, called securitization, occurs when a speaker performs a discursive action, "speech-act," claiming that the issue constitutes a security matter, and is successful when the relevant audience accepts this claim, thus legitimating the use of exceptional measures as a response. (Buzan, de Wilde and Waever, 1998) While I argue that this is an oversimplified interpretation of the process, I use this theory as a point of departure for my research and attempt to use my case study to illustrate the merits of a more comprehensive understanding of securitization. Based on the CoS's emphasis on the discursive element of the securitization process, I have asked: how is the US-Canada border being securitized in American political discourse? I have conducted a discourse analysis of statements made by President George W. Bush and the Department of Homeland Security within the period beginning with the signing of the Intelligence Reform and Terrorism Prevention Act in December, 2004 and ending with the signing of the Security and Prosperity Partnership of North America in June 2005. I have sought to understand how these speakers participate in the securitization of the US-Canada border, analyzing the discursive tools they have adopted, the contexts within which they speak, and the way they structure their claims. The results of my analysis have led me to conclude that, first, the securitization process as a whole must not be understood as a singular speaker performing a singular speech-act in a singular moment accepted by a singular audience, but rather as the on-going interaction between varying relevant actors who participate in creating momentum or resistance within an issue's securitization. Secondly, I conclude that within the securitization of the US-Canada border, the two speakers included in this research participate in the perpetuation of the process through both what is said -- primarily the identification of the terrorist threat, but also the inclusion of borders in larger, existing security contexts -- and what is not said -- the absence of details and definitions, as well as the choices made by the speakers in terms of the types of evidence provided. Taken together, these findings illustrate the importance of considering a more complex understanding of the securitization process and create an opportunity for an expanded research project that will include an analysis of activities performed by a wide range of actors.

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)0.012
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 score0.135
Threshold uncertainty score0.977

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.007
Science and technology studies0.0440.070
Scholarly communication0.0220.010
Open science0.0020.008
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.344
Teacher spread0.329 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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