<i>Be like</i>and the Constant Rate Effect: from the bottom to the top of the<i>S</i>-curve
Bibliographic record
Abstract
The be like quotative emerged rapidly around the English-speaking world and has quickly saturated the quotative systems of young speakers in multiple countries. We study be like (and its covariants) in two communities – Toronto, Canada, and York, United Kingdom – in apparent time and at two separate points in real time. We trace the apparent-time trajectory of be like and its covariants from inception to saturation. We take advantage of the prodigious size of our dataset to examine understudied aspects of the linguistic factors that condition quotative variation. Building on earlier suggestions (Cukor-Avila 2002; Durham et al. 2012) that be like might show patterning over time consistent with the C onstant R ate E ffect (or CRE, Kroch 1989), we argue that the CRE does indeed apply to the rise of be like , but needs to be handled with care. Logistic modelling assumes that the top of the S -curve is located at 100 per cent of a given variable context. In the case of be like , the saturation point is nearer 75–85 per cent, with minor variants retaining small semantic footholds in the system. In conjunction with our analysis, we suggest how to adapt the predictions of the CRE to changes likely to lead to saturation but not categorical use.
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How this classification was reachedexpand
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 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".