Bibliographic record
Abstract
This paper proposes a renewed and more textured understanding of the relation between deixis and direct discourse, grounded in a broader range of genres and reflecting contemporary multimodal usage. I re-consider the phenomena covered by the concept of deixis in connection to the speech situation, and, by extension, to the category of Direct Discourse, in its various functions. I propose an understanding of Direct Discourse as a construction which is a correlate of Deictic Ground. Relying on Mental Spaces Theory and the apparatus it makes available for a close analysis of viewpoint networks, I analyze examples from a range of discourse genres - textual, visual and multimodal, such as literature, political campaigns, internet memes and storefront signs. These discourse contexts use Direct Discourse Constructions but usually lack a fully profiled Deictic Ground. I propose that in such cases the Deictic Ground is not a pre-existing conceptual structure, but rather is set up ad hoc to construe non-standard uses of Direct Discourse–I refer to such construals as Fictive Deictic Grounds. In that context, I propose a re-consideration of the concept of Direct Discourse, to explain its tight correlation with the concept of deixis. I also argue for a treatment of Deictic Ground as a composite structure, which may not be fully profiled in each case, while participating in the construction of viewpoint configurations.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".