A model of collaborative immigration advocacy to prevent policy-based trauma and harm.
Bibliographic record
Abstract
OBJECTIVE: Research suggests that antiimmigrant policies enacted in the United States, magnified during the 2016-2020 period, propagate widespread trauma across communities of immigrants (von Werthern et al., 2018). While these policies harm all groups of immigrants, structural conditions (e.g., lack of documentation status, race, ethnicity, country of origin, and other social and legal determinants) shape how they are experienced. To address the widespread traumatic harm inflicted by racist and xenophobic policies, a group of leaders from eight Divisions of the American Psychological Association (APA) and the National Latinx Psychological Association (NLPA) launched an Interdivisional Immigration Project (IIP). METHOD: The IIP served to develop a model for collaborative advocacy, bringing together mental health providers (i.e., psychologists, social workers), allied professionals, and immigration activists from community organizations across the country. This model was developed over the course of 1 year, coinciding with the global coronavirus disease 2019 (COVID-19) pandemic and the amplified movement for racial justice. RESULTS: This article describes the key components of the IIP collaborative advocacy model: (a) structuring leadership in a democratic and egalitarian manner, (b) centering and uplifting immigrant voices, (c) forming teams across five U.S. regions, (d) facilitating critical dialogues grounded in liberatory practices, (e) centering trauma and empowerment, and (f) developing advocacy strategies. The IIP collaborative advocacy model is informing advocacy to protect immigrants from harm. DISCUSSION: This model may be used as the basis for ongoing humane immigration policy activism that centers the voices of community activists, and that pushes psychologists and allied professionals to use their positionality to support community-based efforts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".