Bibliographic record
Abstract
The controversy surrounding the description of tense in English has remained because scholars have concentrated on carving descriptive niches for themselves rather than paying appropriate attention to its causative factor(s), resulting in three different descriptions: traditional, structural, and systemic. This paper identifies the genesis of the problems, points out how this hinders the attainment of descriptive accuracy, and proffers some solutions. It contends that arguments, such as whether or not there is a future tense for English, stem from the way tense is generally conceptualised. It examined ten standard definitions of tense and found that the keyword grammaticalisation is narrowly interpreted to mean the morphological only, whereas a language’s grammatical system consists of both syntactical and morphological aspects. The non-recognition of the syntactical component—even by grammarians that acknowledge future tense—is the root of the descriptive issues with tense. The paper proposes syntactical marking, achieved by placing the auxiliary WILL/SHALL or BE GOING TO before the base form verb, as the mechanism for future tense marking in English. In effect, English has a three-tense system, and its modes of marking are morphological (for present and past tenses) and syntactical (for future tense).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".