Pragmatic Aspects of Tense in English and Arabic: Based on a Neo-SRE Theory
Bibliographic record
Abstract
The hypothesis upon which this paper is based is that in both Arabic and English the notion of tense underdetermines the notion of time, and some pragmatic enrichment is needed to the get at the correct temporal interpretation. In both languages, beside the normal unmarked tense usages, some marked usages of tense are available wherein the tense constructions do not refer to their equivalent temporal intervals; this is done for the sake of rhetorical purposes as illustrated and exemplified. Even the unmarked cases to tense are proven to require, for sound interpretation, the inclusion of pragmatic givens. Many examples are given in both languages showing the pragmatic nature of the temporal interpretive process of tense in terms of the SRE theory where the interrelationship of the three-time intervals speech, event, and reference times (S/TU, E/TSit, R/TT) is based primarily on rather pragmatic parameters within the process of temporal interpretation. Some new treatment is given concerning the theory of tense interpretation which is related to a pragmatic conception of the speaker’s temporal projection or “virtuality” via which tenses’ inherent three-time points are pragmatically interrelated and arranged in terms of the potential existence of multiple virtual and non-virtual speakers.
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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 | 0.002 | 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 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".