Bibliographic record
Abstract
I suppose everybody else has their own dialect so we’ve got our own little dialect so. (Barry Brandon, DUR, 010) In this chapter I examine a number of features that involve the way people talk about time (i.e. tense), modality (i.e. ability, permission and obligation) and aspect (i.e. the manner of an action). The tense/modal/aspect system of English has changed dramatically over the past several hundred years. I will focus on three areas. Each one has been involved in extensive variation and change. The first involves changes in the future temporal reference system as the older forms shall and will give way to a newer construction with going to . The second involves reorganization of the modal system, in particular the expression of obligation/necessity. In this case an old modal, must , is fading away as two other forms, i.e. have to and have got to , compete for this meaning. The third involves transformations in the forms used to express stative possession, i.e. ownership and personal attributes. Where once have was the only variant, over the past several hundred years have got has encroached on its territory. Examination of these systems of grammar in the Roots Archive may reveal earlier stages in the development of these areas of grammar. In turn, this may provide a window on how grammars evolve. The future The future is an ideal choice for cross-community analysis in the context of ongoing change. Its major variants, going to and will (often ’ll ), as in (1), are widely used and shared by most, if not all, varieties of English. Although people sometimes think there is a meaning difference between these forms, in running conversation they are often interchangeable, as in the examples in (1).
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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 | 0.001 |
| 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 | 0.013 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".