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Economic Costs, Economic Benefits, and Attitudes Toward Immigrants and Immigration

2011· article· en· W1584172056 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.

Bibliographic record

VenueAnalyses of Social Issues and Public Policy · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicCulture, Economy, and Development Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsImmigrationEconomic costCompetition (biology)PerceptionEconomicsPolitical scienceBusinessDemographic economicsPsychologyLaw

Abstract

fetched live from OpenAlex

Perceptions of economic costs and benefits play an important role in determining attitudes toward immigrants and immigration. The Unified Instrumental Model of Group Conflict, and the correlational and experimental research supporting it, indicate that when immigrants are seen as competing with members of the host society for economic resources, negative attitudes toward immigrants and immigration result. Yet measures taken to reduce this perceived competition and threat can have unforeseen consequences. Recent bills intended to reduce illegal immigration in U.S. states, such as Arizona's Senate Bill 1070 and Georgia's House Bill 87, have been framed by supporters as intended to reduce the economic costs of illegal immigration. Their consequences, however, have been increased economic hardship in the form of economic boycotts and lost farm production. We suggest that recognizing the mutual dependency between immigrants and members of host societies may be a first step in reducing support for harsh measures against illegal immigration, to the benefit of all.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score0.917

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.093
GPT teacher head0.369
Teacher spread0.275 · 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