Agenda Setting and Immigrant Politics
Bibliographic record
Abstract
The authors identify and analyze patterns of community organizing among Latin Americans in Toronto for the period from the 1970s to the 2000s as part of a broader analysis of Latin American immigrant politics. They draw on the concept of social fields to map Latin American community politics and to capture a wide range of relevant organizations, events, and strategic moments that feed into the constitution of more visible and formal organizations. Five distinct waves of Latin American migration to Toronto produce three types of community organizations: ethno-national, intersectional panethnic, and mainstream panethnic groupings. This migration pattern also leads to a layering process as established organizations evolve and new migrant groups with specific priorities and ways of organizing emerge. The authors present a case study of the development and agenda-setting process of the Centre for Spanish Speaking People, a mainstream, multiservice, panethnic organization. Agenda setting is defined as the process of defining the vision and mission of an organization or cluster of organizations. The case study captures how a mainstream panethnic organization mediates between diverse in-group agendas of Latin American immigrants and out-group, specifically, state-generated, agendas, and how this agenda-setting process changes over time in tune with shifts in the political opportunity structure. The authors propose, however, that agenda setting is a dialogic social process that involves more than navigating the existing political opportunity structure. Agenda setting involves in-group and out-group dialogues embedded within a complex organizational field. It is an instance of political learning. The analysis of these dialogues over time for a specific group and organization captures immigrant politics in practice.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".