Complement Clause Constructions in Flux: Insights from Corpus-Based Analysis
Bibliographic record
Abstract
Complement clause constructions in English are evolving. This study demonstrates that shifts in speakers’ focus of consciousness can induce changes in a construction’s complement clause, affecting both its syntax and semantics. The construction [This is the [ordinal number (ON)] time (that)] (e.g., This is the first time (that)) typically employs the present perfect, as in This is the first time that I’ve heard her sing (Swan, 2017). However, corpus data from English-Corpora.org reveal that the pattern [This is the ON time (that) + S + present tense or present progressive] is also attested (e.g., …this is the first time you see formal territorial markers…, COCA, 2006, ACAD), reflecting shifts in speaker perspective and current experiential focus, despite the construction not ordinarily requiring these tenses. This study adopts a qualitative, corpus-based approach to describe and interpret these non-canonical patterns, focusing on [This is the first time (that)]. Emphasis is placed on mapping meaning and speaker viewpoint onto observed constructions, rather than on quantitative prevalence. The analysis clarifies how the function of [This is the ON time (that)] shifts with its complement clause, illustrating the interaction between construction and tense/aspect—i.e., colligation—and providing a precise, comprehensible account of ongoing changes in English complement clause constructions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".