The Phoenix Rises from El Cenizo: A Community Creates and Affirms a Latino/a Border Cultural Citizenship through Its Language and Safe Haven Ordinances
Bibliographic record
Abstract
When a small Texas border town of seven thousand, El Cenizo, adopted an ordinance mandating that all city functions, meetings and notices shall be conducted and posted in predominant language of community, which is Spanish, a media firestorm ensued. The Ku Klux Klan threatened to burn town down and have its residents sent back to Two nationally syndicated disc jockeys (shock jocks) based in Virginia called El Cenizo City Hall while on air, and publicly insulted a city commissioner telling her that anyone who will not or cannot speak English should get on their burros and go back to Mexico. Critics have called El Cenizo the next Quebec, the canary in mine, and have described city's actions as benign disassimilation. The Mayor and Commissioners of El Cenizo defended their actions by indicating that they did so at request of community. They note that as a result of ordinance, they have seen an increase in level of civic participation of their constituents, In addition, residents now feel safer in their community because of a Safe Haven ordinance. This ordinance, enacted on same day City passed Predominant Language Ordinance, prohibits City's elected officials and employees from disclosing, investigating or requesting information concerning a resident's immigration status. This article analyzes constitutionality of both ordinances, contextualizes them as a form of Latino cultural citizenship and finally, offers some lessons to be learned from what happened in El Cenizo.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".