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Record W2041051211 · doi:10.1177/0020881713504669

Insecurity and Economic Inequality in the United States

2012· article· en· W2041051211 on OpenAlexaboutno aff
Satya R. Pattnayak

Bibliographic record

VenueInternational Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsInequalityTerrorismLegislationImmigrationSecuritizationPolitical scienceCapital (architecture)Development economicsPolitical economyEconomic inequalityEconomicsEconomic growthGeographyLawFinance

Abstract

fetched live from OpenAlex

In recent memory, the United States has lived under a heightened sense of insecurity. This sense of insecurity, which initially emanated from the terrorist attacks in September 2001, subsequently has been extended to the employment and financial sectors. As a response to terrorism, multiple Congressional laws have put added restrictions on the movement of humans, capital, and goods and services. On the whole, such policy changes have contributed to a climate of securitization of the major areas of American life. The sense of insecurity has been correlated with a level of economic inequality previously unseen in American history. It is argued that to address both issues simultaneously, the United States should foster improved relations with both Mexico and Canada. The pending legislation in the US Congress on comprehensive immigration reform may hold one of the keys to addressing the problems of both insecurity and inequality in a meaningful way.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.662

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.003
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
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 head0.089
GPT teacher head0.396
Teacher spread0.308 · 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 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

Citations1
Published2012
Admission routes1
Has abstractyes

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