Bibliographic record
Abstract
Spanish teachers in the USA are responsible for showing students what Spanish looks and sounds like (Ballman, Liskin-Gasparro & Mandell, 2001) and therefore act as role-models for their students in terms of their attitudes towards different varieties of Spanish. They must choose which features from which varieties to teach their students (Burns, 2018). Spanish teachers in the UK found Caribbean Spanish difficult to comprehend (Bárkányi & Fuerte Gutiérrez, 2019) and Spanish teachers in the USA preferred Peninsular Spanish over other varieties (Martínez-Franco, 2019), similar to Spanish teachers in Australia (Ortiz-Jiménez, 2019). The current study investigates (dis)preferences towards different regional varieties of Spanish by 63 primary, secondary and postsecondary teachers of Spanish in the USA. The findings indicate preferences split among four macro-varieties and a dispreference for Caribbean Spanish, highlighting the importance of comprehension and exposure to varieties regardless of prior explicit training on the topic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".