Language as Investment, Capital, and Economics: Spanish-Speaking English Learners’ Language Use and Attitudes
Bibliographic record
Abstract
Drawing on the notion of investment in language and identity, the concept of language as capital, and the theory of language as part of economics, this study explores California high-school Spanish-speaking English learners’ use of Spanish and English at home, at school, and in the ESL class, and their perceptions on these two languages. Analysis of 37 survey responses reveals that the participants did not have an either-or attitude toward the languages they spoke and concurrently claimed frequent use of and even fluency in the societal language and their heritage language. They did not have a simplistic notion of linguistic identity and simultaneously claimed the English-speaking identity, the Spanish-speaking identity, and the bilingual identity. The results indicate that, rather than a sign of second language insufficiency, bilingual language use in and outside of the ESL class served as an intentional investment in language development and maintenance, identity construction, and preparation for participation in the multilingual marketplace in the internationalized new economy. ESL instructors are encouraged to recognize and acknowledge the role of bilingual language use in class and to create a supportive classroom environment that builds on the linguistic and cultural capital of English learners and fosters the development of both languages into literate, academic and professional capacity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".