The present perfect from a diachronic perspective: an analysis of aspectual and tense constructions
Bibliographic record
Abstract
According to some historical linguists, the early English HAVE + past participle construction had only one meaning (e.g. Mustanoja 1960: 499-500, Carey 1994), namely the resultative perfect. However, recent research has demonstrated that the Old English HAVE + past participle construction has much more in common with the Modern English present perfect than was previously thought (Lee 2003 and Łecki 2010). Reading of the literature of both diachronic and synchronic analyses opens up terminological issues of the usage of “uses”, “meanings” and “functions”. Such terminological ambiguity is in fact a reflection of the uncertainty as to the true nature of HABBAN + past participle construction or the present perfect in Modern English. A recent synchronic corpus-based analysis of the present perfect (Žic Fuchs 2009) has shown that we are faced with four constructions each reflecting a specific meaning, the most frequent two being the resultative and the experiential, which are primarily aspectually marked. The other two meanings, the perfect of persistent situation and the perfect of recent past, exhibit lower frequency counts and features of relative tense. This study differs from views expressed by authors such as Klein (1994) and Declerck (2006) have gone to great lengths to prove the status of the present perfect as belonging to the tense system. On the other hand, Lyons (1968: 315-316) and Comrie (1976) see it predominantly as aspect. On the basis of an extensive analysis of HAVE + past participle constructions in Old and Middle English corpora (The York-Toronto-Helsinki Parsed Corpus of Old English Prose and Penn-Helsinki Parsed Corpus of Middle English, Second Edition), the development of aspectual and relative tense meaning constructions will be demonstrated. Frequency of occurrence of different meaning constructions will be compared to contemporary evidence found in Modern English corpora. Thus the basic aim of this paper is to provide an overview of the development of meaning constructions that we find in Modern English present perfect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".