Disruptions due to COVID-19: using mixed methods to identify factors influencing language maintenance and shift
Bibliographic record
Abstract
Abstract Around the world, COVID-19 lockdowns have caused abrupt shifts in the amount of time spent at home versus out of the home for work, school, and recreation. As a result, many individuals have experienced a disruption in the frequency and type of their interactions. Given the importance of intergenerational transmission and intergenerational interaction for promoting language maintenance, and the importance of peer-to-peer interaction for promoting language shift, we ask how these abrupt changes necessitated by social distancing will affect language use and attitudes, specifically short- and long-term language maintenance or shift involving heritage languages. We examine principles of language maintenance and shift in the context of the COVID-19 lockdown for university students, people still involved in critical acts of identity creation. Here we describe a survey designed to learn how the lockdown is affecting young people’s language ecologies and attitudes. Using both quantitative and qualitative interpretive methods, we document the experiences of over 400 students, focusing on changes in their perceptions of their language use and the causes of these changes.
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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 | 0.048 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".