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Record W2486725325 · doi:10.1057/9781137509758_4

Techniques of the Self in the Face of Precarity

2015· book-chapter· en· W2486725325 on OpenAlexaff
Tanya Basok, Danièle Bélanger, Martha Luz Rojas Wiesner, Guillermo Candiz

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2015
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversité LavalUniversity of Windsor
Fundersnot available
KeywordsPrecarityCitizenshipBiopowerNarrativeAdversaryFace (sociological concept)Context (archaeology)SociologyGender studiesPolitical scienceGeographyPoliticsSocial scienceArtComputer scienceComputer security

Abstract

fetched live from OpenAlex

Drawing on Foucault’s notion of the ‘techniques of the self,’ this chapter illustrates various techniques migrants employ to overcome the paralysing effect of precarity, shaped in the context of the United States biopolitics of citizenship, on their mobility. On the basis of migrants’ narratives, the chapter discusses such techniques of the self as spirituality, self-concealment, ‘passing as Mexicans’, outmanoeuvring the enemy, the art of self-preservation, and the art of vigilance. It illustrates that migrants acquire knowledge of these techniques from their own experiences or from those of other migrants. Migrants train themselves to become resilient and resourceful. Yet, as the chapter maintains, these techniques cannot guarantee that migrants’ mobility will not be disrupted. Migrants’ journeys consist of intersecting and interchangeable patterns of mobility and immobility, and the two may co-exist: migrants continue to plan and prepare for their journeys while remaining (at least temporarily) immobilized. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.031
Scholarly communication0.0050.006
Open science0.0010.004
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.035
GPT teacher head0.292
Teacher spread0.257 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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Same venuePalgrave Macmillan UK eBooksSame topicMigration, Refugees, and IntegrationFrench-language works237,207