Ambivalent Resonance: Advocacy for Secure Status for Migrant Farm Workers in Spain, Italy and Canada during the COVID-19 Pandemic
Bibliographic record
Abstract
Drawing on insights from scholarship on contentious action frames, this article examines the framing of demands for social justice for migrant farmworkers in Spain, Italy and Canada during the COVID-19 pandemic. We focus particularly on how activists in each country aligned their action frames with prevalent public discourses on the essential contribution migrants make to agricultural production, the need to guarantee “health for all,” and “increased vulnerability” of migrants’ lives during the global health crisis. Using these diagnostic frames, activists in the three countries called for secure legal status for all migrants. Drawing on the literature on contentious action frames, we then analyze if action frames advanced by activists during the COVID-19 pandemic “resonated” with the understanding of these issues by policymakers. We challenge an approach to understanding resonance in binary terms as either present or absent. Instead, we introduce the notion of “ambivalent resonance” to draw attention to the fact that some frames are accepted only partially or only by some policymakers but not the others, as was the case in the three countries under study. We then situate this ambivalent resonance in the context of immigration priorities and recent trends in immigration policy development in these three countries and suggest that activists can build on ambivalences to advance migrant rights to status.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.025 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".