Bibliographic record
Abstract
Services designed to facilitate immigrant integration and civic-political engagement in the United States are highly privatized compared to those in Canada, where state funding provides the bulk of funding for immigrant needs, leading to a political context in which social welfare for immigrants is thin but opportunities to challenge state policies are perhaps greater. However, the decoupling of federal immigration policies from local integration presents challenges to local actors attempting to influence legislation at the federal level. This article is an exploration of the tensions between local and national organizing for comprehensive immigration reform (CIR) in the United States, with a particular focus on the effects of these tensions among local immigrant community organizations in Miami, Florida. The authors present data gathered from the Immigrant Participation and Immigration Reform project, a national effort to increase the civic engagement of individual immigrants, to build the capacity of immigrant organizations in civic engagement, and to build local-to-national relationships for the purposes of passing CIR. The authors compare two levels of engagement: local community organizing and national collaborations. Using ethnographic data from local and regional organizations in Miami, the authors explore the tensions organizers felt between local and national engagement with immigration legislation and how organizers responded to those tensions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".