Bibliographic record
Abstract
Introduction The foregoing study has involved a corpus investigation of the diachronic development of a number of “comment clauses,” or sentence disjuncts of clausal origin, that are based on common verbs of perception and communication, such as look, see, say , and mean . These comment clauses assume diverse syntactic forms: present-tense verbs with first-person subjects ( I say, I daresay, I see, I mean, I find, I gather ), with second-person subjects ( you say, you see ), and with third-person subjects ( that is to say ), adverbial/relative structures ( as you say, as/so you see, if you will, as it were, as I find, as I gather, what's more ), and imperative clauses ( look, see, hark, listen, hear ). The chapter will first review the theoretical background of this study (§11.2) and will synthesize the results of the case studies in regard to the syntactic sources of comment clauses (§11.3). It will end with a possible interface with another theoretical model as well as some directions for future research (§11.4). Background of the study Several assumptions, which are explored in the first three chapters of this work, underpin the case studies, namely, that comment clauses are parenthetical pragmatic markers of clausal origin (§11.2.1), that in their historical development comment clauses undergo grammaticalization (§11.2.2), and that in contrast to the well-understood development of non-clausal pragmatic markers, the prevailing view of the origin of comment clauses – what I have termed the “matrix clause hypothesis” – is problematic in a number of ways (§11.2.3).
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.155 | 0.033 |
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".