A linguistic phoenix: The recycling of <i>very</i> in Ontario English
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 For seven years in a row (2016 through 2022), we carried out a project with two goals. One was to train undergraduate students in sociolinguistic interviewing; the other was to catch change among English intensifiers. We expected to find an innovative variant, maybe either so or super . However, the incoming form we identify is very . We propose that, after a long decline, very became unusual enough to gain novelty value and be available for recycling. This surprising finding emerges clearly from our fine-grained, real-time data across two registers (speech and instant messaging) despite dozens of different student interviewers and two years of pandemic conditions. The cohesive patterns attest to the fundamental orderliness of language, even in phenomena such as English intensifiers that are characterized by constant, rapid change.
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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 | 0.002 |
| 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) | 0.000 | 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