Bibliographic record
Abstract
At first glance, the syntax of quotation appears to be a rather straightforward matter of transitivity and complementation. However, quotation raises a number of intriguing and perplexing questions for the functioning, structure, and development of syntactic systems, and for their interactions with the semantic–interpretative interface. The purpose of this review is to articulate and exemplify these challenges as raised in the literature of various linguistic domains, and to highlight the ways in which quotation evokes a range of empirical and theoretical implications. This article begins by discussing the issues faced by traditional syntactic analyses of quotation, then examines the types of changes implicated by this sector: grammaticalization, lexicalization, and systemic change and variation. The view that emerges is that approaches that privilege the syntax as the sole structure-building component of the grammar are insufficient for accounting for the linguistic and discourse–pragmatic facts; advances in understanding this linguistic system necessarily require a more holistic approach that incorporates both intrinsic and extrinsic factors.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.006 | 0.021 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".