Bibliographic record
Abstract
In this paper, I argue for an approach which treats perspective-taking and viewpoint as conceptual patterns prompted by a range of linguistic forms. I show that commonly discussed perspective-taking phenomena cannot be represented in sufficient depth by looking, on the one hand, at local sentence-level issues of disambiguation and, on the other hand, at the “common ground” explanations pertaining to some global communicative context. At the same time, I show that viewpoint phenomena are pervasive in language, rather than being limited to specific instances. The main argument is that in most instances linguistic expressions represent multiple viewpoints, rather than just one, and that these multiple viewpoints form coherent networks. The paper analyses a number of examples to explicate the nature of viewpoint networks and the mechanisms which lead to interpretation of discourse on their basis. To illustrate these points, I discuss examples from discourse, constructions which specialize in profiling viewpoint configurations (for example, various forms of reported speech, etc.), and grammatical forms (such as tense, pronouns, and determiners). The argument is additionally supported by data from gesture and newly emerging forms of online communication.This article is part of the special collection: Perspective Taking
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".