Review of the English Tense System: Decoding Dichotomies and Restructuring Instructional Practice
Bibliographic record
Abstract
As far as the main purpose of teaching and learning of the Grammar of a language is concerned, it should tell the teachers and learners the principles and parameters of sentence construction in the given language, i.e. English Language in the context of the discussion in this paper. Incidentally, the grammatical device of tense becomes more important and relevant at the level of discourse and communication. However, a predominantly common approach to teaching and learning of the system of tense in English language has been to understand it in synonymous terms with the notion of three timelines of present, past and future, which poses situations of systemic difficulties and makes it confusing and misleading to comprehend and communicate sentences and utterances in terms of communicative clarity within the parametric confines of the linguistic system of the English Language. Focusing on this issue, this paper demonstrates the ways to unfold the dichotomies involved in the traditional ways of teaching and learning of the grammar of tense, times and aspects of verbal action in English Language and suggests an instructional framework to resolve the related pedagogical issues of concern.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".