Still a small World?: critical analysis of <i>cultura</i> in secondary Spanish world language textbooks
Bibliographic record
Abstract
This critical teacher action research study revisits previous findings that high school Spanish textbooks tend to reflect small and invented worlds, rely on cultural stereotypes, and fail to engage students in critical thinking about sociocultural issues. Using multimodal SFL-based critical discourse analysis, we explore more recent textbooks used in the United States, with a focus on how they construe meanings regarding Spanish speakers, cultures, and linguistic/cultural dominance; and how they position and engage students relative to Spanish language varieties and cultures. Findings from an analysis of 24 textbook passages demonstrate that the focal textbook series linguistically and visually backgrounds Spanish speakers, particularly Black and Indigenous members of Spanish-speaking communities, as contributors to culture and construct membership in a nation-state as the most salient aspect of their cultural identities. Moreover, the textbooks position and engage students as elite bilingual tourists and cultural consumers. We discuss how these semiotic choices reproduce deficit raciolinguistic ideologies around Spanish speakers and cultures in the U.S. context, which has implications for world language teachers wishing to challenge these ideologies and simultaneously support students’ development of language and critical literacies.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".