Language switching in the thinking processes underlying second-language composing task performance among mandarin-english bilinguals in the context of computer studies
Bibliographic record
Abstract
The dialectical relationship between language and thought has been closely scrutinized from various perspectives in philosophy and psychology. The present dissertation examines this relationship in the context of bilingualism. Numerous studies have shown that bilinguals frequently switch between their two languages in the process of composing tasks in a second language. However, little is known about when and why bilingual people switch languages in these situations, and how language switching relates to their thought processes as well as to their success in second-language tasks. I examined, within a theoretical framework of language of and for thought, the qualities of bilingual thinking demonstrated by bilingual students during their verbal reports on the performance of their second language tasks with a view to identifying the nature of language switching in their cognitive processing. 20 Mandarin/English bilinguals with different levels of second language (English) proficiency studying at a college in Ontario produced concurrent verbal reports while composing two types of second-language tasks in the context of their computer science studies. A retrospective verbal report was also elicited from each participant during a follow-up interview. Their written texts were evaluated by independent college course instructors based on a task-relevant scale. The think-aloud protocols were analyzed in terms of idea units and language switching. I analyzed the thinking contexts and propositional structures underlying specific types of thought, and participants' perceptions of these, using two-way repeated ANOVA and correlations, to assess differences and commonalities. Different qualities of thinking prompted different uses of first and second languages. The extent of the first language use correlated positively to success in the second language tasks. The findings further suggest that language switching in composing during second-language task performance relates to levels of cognitive demands associated with specific categories of thought. These findings challenge certain theoretical assumptions about the factors influencing language switching and provide important implications for theories, research, and pedagogical practices vis-a-vis bilingual cognition and second-language education in general. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".