Grammaticalization at an early stage: future<i>be going to</i>in conservative British dialects
Bibliographic record
Abstract
The English go future, a quintessential example of grammaticalization, has shown layering with will since at least 1490. To date, most synchronic evidence for this development comes from dialects where be going to represents a sizable proportion of the future temporal reference system. However, in the United Kingdom in the late twentieth century there were still dialects where be going to was only beginning to make inroads, representing a mere 10–15 per cent of future contexts. These varieties offer an effective view of the early stages of grammatical change. Statistical analysis of nearly 5,000 variable contexts reveals that the use of be going to is increasing across generations, but at different rates, depending on location and orientation to mainstream norms. Major patterns of use mirror previous findings: be going to is favoured for subordinate clauses. However, other widely reported constraints conditioning be going to are radically different across age groups, exposing contrasts between incipient vs later stages of grammaticalization. In the most conservative dialects be going to is strongly correlated with negatives and questions especially in the first person singular. This suggests that these contexts may have been the ‘trigger’ environments for redistribution of meaning of the incoming grammatical form (Hopper & Traugott 1993: 85). The fact that strong effects of negatives and questions endure in contemporary urban varieties (Torres-Cacoullos & Walker 2009) confirms that grammaticalization begins in very specific syntactic contexts, and impacts on the system for generations to come. In contrast, other reported constraints – resistance of be going to in the first person singular and extension to inanimates and far future readings – emerge across generations, suggesting they are later developments. Taken together, these findings demonstrate how synchronic dialects show us incremental steps in the grammaticalization process. Comparative sociolinguistic analysis thus offers insights into which patterns define the point of grammaticalization itself; which derive from systemic processes; which can be attributed to discourse routines and collocations; and how these factors converge in shaping the evolution of grammar.
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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 | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".