The changing<scp>future</scp>: competition, specialization and reorganization in the contemporary English future temporal reference system
Bibliographic record
Abstract
The English future temporal reference system has long been recognized as a variable system undergoing change. The main variants in contemporary English ( will and be going to ) have both been argued to have gone through (and to potentially still be undergoing) grammaticalization. At the same time, be going to has been gradually increasing in frequency relative to will over the last 500 years. However, investigation of the ongoing development of this system has been sparse. This article makes use of a large contemporary sociolinguistic corpus of a mainstream variety of North American English and the apparent-time construct. Several factors that have been implicated in the development of this system (Sentence Type, Clause Type, Proximity, Verb Type, and the Animacy and Grammatical Person of the Subject) are considered and a multiplex series of changes are uncovered. Underlying an overall, albeit slow, change in frequency towards be going to , we find evidence for specialization of one or the other variant in different linguistic contexts, neutralization of a constraint consistent with ongoing loss of variant nuances through semantic bleaching, and the persistence of constraints consistent with morphological doublet competition.
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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 | 0.001 | 0.002 |
| 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 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| 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".