“There’s always racism”: Puerto Rican Mothers Naming Linguistic Inequities and Sharing Community Cultural Wealth Post-Displacement
Bibliographic record
Abstract
In the following study, displaced Puerto Rican mothers and I created and explored a learning space – culture circles – that engaged participants in a critical cycle of problem posing, dialogue, and problem solving in relation to their experiences in the receiving Pennsylvania community. Using a qualitative, ethnographic approach, the study drew from Critical Pedagogy (CP), raciolinguistics, and a Community Cultural Wealth (CCW) framework to inform data collection and analysis. Individual interviews, culture circle meeting recordings, field notes, and digital artefacts created by four focal participants help illustrate how these mothers navigate the US school system and the wider receiving community in southern Pennsylvania. Moreover, the data reflect how racism in this context is based predominantly on families’ language practices. Despite mothers facing significant challenges due to race/ethnicity, language, class, and gender, this study highlights the conversations that displaced Puerto Rican mothers engaged in regarding the impact racism had in their everyday lives. Furthermore, the space we created allowed participants to share and model their accumulated linguistic, social, and resistant capital for their children’s academic success.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".