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
Mobility is the most effective leveller of dialect and accent, and mobility constitutes a powerful linguistic force today. The sociolinguistics of mobility unites several disparate threads in my own research. First, immigration represents extreme mobility, and societies with profuse immigration differ in partly predictable ways linguistically and culturally from those with little or no immigration. Second, dialect acquisition by the children of newcomers provides new perspectives on critical period effects and influences, including the Ethan Experience, in which the nativization of children is abetted by their imperception of foreign‐accent features in their parents’ speech. Third, identification of relatively recently‐arrived people from other dialect regions allows comparisons of their linguistic norms with the communal norms, and a measure of their linguistic influence. From the cumulative results, we are in a position to frame hypotheses about linguistic variables in terms of their susceptibility to change and their resistance to it, and the identities of inhibitors and accelerators. All these threads should ultimately form integral aspects of the dynamics of dialect convergence.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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