Bibliographic record
Abstract
This investigation is part of the authors ’ larger research project on so-called minor declarative complementizers in the history of English, that is, connectives recruited mostly in the adverbial domain that are occasionally used in complementation. The present study sheds light on the complementizer use of the three originally comparative links as if, as though, and like in Present-Day English complement structures. In the theoretical part of the article, the authors argue for the complement analysis of certain clauses depending on as if, as though, and like (e.g., It seemed as if the strange little man had never been there). The empirical part of the study analyzes data drawn from the Brown family of corpora (LOB, Brown, FLOB, and Frown), the Diachronic Corpus of Present-day Spoken English (DCPSE), and the Toronto English Archive (TEA), which are representative of both written and spoken language at different time periods (1960s, 1990s, and early 2000s) and in different varieties of English (British English, American English, and Canadian English). Taking the corpus data as a starting point, and with the aim of revealing what ongoing change is observable in the contemporary language,
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.170 | 0.040 |
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".