Looking for the causal values of as and since in large corpora, and how these values compare with each other
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article deals with English causal subordinate clauses introduced by since or as. Both these markers may convey different meanings according to contextual variations, and can express temporal relations, from which their causal value is derived. The semantic closeness between as and since whenever they express a causal relation makes it necessary to harvest a large number of attested examples in order to compare and contrast them. The recourse to large on-line corpora such as the British National Corpus gives rise to specific practical difficulties, however, especially as far as as is concerned; because of the high frequency of the subordinators in question, one is confronted to thousands of examples, few of which turn out to have a clear causal value in the case of as, hence the recourse to other means in order to make up a large enough corpus of examples.The concept of presupposition is examined, as it has often been argued that causal since subordinates, unlike as subordinates, introduce a presupposed content. But when confronted to a large number of examples, this criterion falls short of accounting for the subtle differences between the two conjunctions. A more theoretical approach is required; thanks to the tools provided by A. Culioli’s Theory of Enunciative and Predicative Operations, it becomes possible to formalise the hypothesis that a since causal relationship is presented as unproblematic and is addressee-oriented, whereas an as causal relationship is felt as unproblematic, and is speaker-oriented.This difference can be felt in the fact that it seems to be easier to find examples of causal as clauses in the press, while the causal use of since is more widespread in general, and does not seem to be specific to a genre in particular. In order to put this impression to the test, I have resorted to the multi-lingual parallel corpus Intercorp.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it