Bibliographic record
Abstract
Recent work in mental spaces theory describes ways in which mental spaces networks represent subjectified and intersubjective meanings. Verhagen (2005) discusses constructions which evoke negotiation of construal across different subjectivities (e.g., extraposed 'that'-clauses, adverbial conjunctions). Sweetser and Ferrari (forthcoming) argue that subjectification involves migration of epistemic stance upwards from its original space. In the paper, I focus on the interaction between subjectified and intersubjective construals, using data from blogs and journalistic prose.Common lexical expressions of epistemic stance ('think', 'doubt', or 'know', epistemic modals, epistemic disjuncts such as 'surprisingly') are naturally interpretable in the way suggested by Sweetser and Ferrari. Syntactically, stance markers appear sentence- (or VP-) initially, and disjuncts are additionally separated by intonation ('I think/doubt/know X', 'He can be hired.', 'Surprisingly, X'). The stance-marking uses of 'I think' or modals have been described as grammaticalized expressions of subjectified and interpersonal meanings (Sweetser 1990, Thompson and Mulac 1991, Traugott 1989, 1995, Vandelanotte 2005). However, the difference between subjectified and intersubjective meanings has not been fully clarified.The difference can best be revealed when stance expressions co-occur with networks involving alternative spaces. Alternative spaces appear primarily in negation (Fauconnier 1985/1994), as well as in predictive conditionals and or constructions (Dancygier and Sweetser 2005). Any interpretation of the negated sentence 'There is no need to worry' relies crucially on the alternative space where worrying is viewed as necessary. Similarly, a conditional sentence such as 'If Tom applies, he can be hired', constructs an alternative scenario (no application, missed opportunity). In the paper, I discuss the cases where stance interacts with negation.Negation is commonly used to affect stance (rather than factual information). 'He can't be hired', 'I don't think X' or 'Not surprisingly, X' all use negation with reference to epistemic stance, and not to the content of the clause (though advocates of Neg-Raising disagree). However, negation interacts independently with different levels of stance marking. A blogger commenting on a song writer's skills might say 'I don't think he can write songs' or 'I think he just can't write songs', to distinguish her subjective doubt from a negative evaluation of the writer's abilities, which shows that the two levels of stance marking are independent. In the actual blog, she wrote 'I don't think he can't write songs', to distance herself from the contextually available criticism of the writer. This intersubjective reading is available primarily in the cases where the speaker's negative stance has another subject's expression of negative stance in its scope. The intersubjective level is thus the top level in the set-up. Negation can be extracted to a yet higher level in constructions like 'It's not that I think/I don't think X', 'If not for X', 'It can't be true that X'. The differences in interpretation which arise from negative stance affecting any of the subjectified levels or migrating further upwards to the intersubjective level can be explained through what I refer to as 'stance-stacking'. The varying directions of migration and projection of stance then explain the resulting constructional patterns.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.021 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".