Disruptions due to COVID-19: using mixed methods to identify factors influencing language maintenance and shift
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it