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Record W3121285661

Partners in Organizing: Engagement between Migrants and the State in the Production of Mexican Hometown Associations

2012· preprint· en· W3121285661 on OpenAlexfundno aff
Natasha Iskander

Bibliographic record

VenueRePEc: Research Papers in Economics · 2012
Typepreprint
Languageen
FieldSocial Sciences
TopicLatin American and Latino Studies
Canadian institutionsnot available
FundersYork University
KeywordsImmigrationGarciaPolitical scienceLegislationState (computer science)Identity (music)Mexican StateGender studiesCommunity mobilizationSociologyPoliticsHumanitiesLaw
DOInot available

Abstract

fetched live from OpenAlex

The massive historic protests in 2006 against anti-immigrant legislation in the United States have sparked renewed interest in immigrant community mobilization. Analysts have turned to Mexican immigrants in particular, not in the least because Mexicans represent the largest immigrant group in the United States by far. In this focus, many scholars and policy makers both have trained their attention on one form of Mexican civic organization that played an important, yet somewhat unanticipated role in the pro-immigrant marches of the mid-2000s: hometown associations, often called HTAs (Bada, Fox, and Selee 2006; Garcia-Acevedo 2008; Portes, Escobar, and Radford 2007). Broadly defined as organizations formed by migrants from a same community of origin (Fox and Bada 2009), they have been roundly lauded as structures that provide migrants with a wide array of support (Ramakrishnan and Viramontes 2010). HTAs have been characterized as organizations through which migrants not only maintain their cultural identity and sustain their affective connection to their hometowns, but also as structures through which compatriots from the same community or region of origin can provide one another with social and material backing in the US (Bada 2011; Orozco 2004).

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 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)0.014
metaresearch head score (Gemma)0.003
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 score0.291
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.089
GPT teacher head0.388
Teacher spread0.299 · 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 teacher head, 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

Citations3
Published2012
Admission routes1
Has abstractyes

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