Intersectionality, Linked Fate, and LGBTQ Latinx Political Participation
Bibliographic record
Abstract
This article uses the concepts of intersectionality and linked fate to understand the relationship between group identification and political behavior among lesbian, gay, bisexual, transgender, and queer (LGBTQ) and non-LGBTQ Latinx individuals. Drawing on the 2016 Collaborative Multiracial Post-Election Survey (CMPS), we find that LGBTQ Latinx respondents report feelings of linked fate to both the Latinx and LGBTQ community, and that LGBTQ Latinx respondents exhibit more political participation than their non-LGBTQ Latinx counterparts. We then find that Latinx and LGBTQ linked fate are significant predictors of participation for non-LGBTQ respondents, and LGBTQ linked fate to predict LGBTQ Latinx participation. Finally, we provide evidence that suggests that feeling linked fate toward more than one marginalized group does not necessarily translate into participation in a greater number of political activities, demonstrating the complexity of group identification for predicting political participation. This study contributes to the theorizing of linked fate and political participation by deploying an intersectional lens that challenges assumptions of Latinx and LGBTQ intragroup political coherence and illuminates the complex effects that different kinds of linked fate have on political participation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".