REAL AND APPARENT TIME IN LANGUAGE CHANGE: LATE ADOPTION OF CHANGES IN MONTREAL ENGLISH
Bibliographic record
Abstract
Linguists often rely on synchronic generational differences in language to supply evidence of language change in progress in “apparent time,”yet this approach must always be evaluated against the possibility that such differences reflect change over speakers' lifetimes (“age grading”), rather than language change. The present paper compares apparent-time data on Montreal English with “real-time” data from earlier studies of the same community, in order to test the assumptions of the apparent-time model. The comparison reveals that, while some age-correlated lexical variables show stability over speakers' lifetimes, clearly suggesting ongoing change, others show changes in progress combined with change over speakers' lifetimes. However, the nature of individual change is generally found to be not the rejection of new variants by older speakers associated with the age-grading model, but late adoption of new variants by adults who learned older variants as children. Most postacquisition change therefore accelerates rather than retards change in progress. Evidence from two phonological variables suggests that late adoption is most characteristic of lexical variation. In any case, the possibility of late adoption implies that an accurate view of language change only emerges when both apparent- and real-time data are examined.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".