So different and pretty cool! Recycling intensifiers in Toronto, Canada
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
This article presents a synchronic quantitative study of the intensifier system in Toronto, the largest urban centre in Canada. The data comprise nearly 10,000 adjectival heads, as in I was so hungry and I was getting really nauseous (TOR/2m). The distribution of intensifiers in apparent time provides startling evidence of change. Very is quickly moving out of favour and really has expanded dramatically. Moreover, there is evidence to suggest that other intensifiers are on the rise – so and pretty . Testing a series of contextual factors known to operate in the development of intensifiers (e.g. adjective function and type) as well as their intersection with social factors (e.g. age and sex) reveals evidence of ongoing delexicalization, but not as part of a continual longitudinal process. Instead, the profile of change reveals recycling, suggesting that the mechanisms of intensifier renewal may be more complex than previously thought.
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 | 0.000 | 0.017 |
| 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