The semantics and pragmatics of English evidential expressions : the expression of evidentiality in police interviews
Bibliographic record
Abstract
The goal of this dissertation is to examine how English speakers express their evidence in the context of police interviews. I show that speakers use discourse markers, in particular, actually, apparently and supposedly, to explain their evidence in a criminal investigation. The data for this research was collected exclusively from transcripts of police interviews of lay witnesses in the investigation into the disappearance and murder of Caylee Anthony that occurred in Orange County, Florida, between 2008 and 2011. I show that actually marks evidence strength and is felicitous where the speaker has the ‘best’ evidence for their proposition. Actually’s evidential contribution largely parallels the best possible grounds evidential -mi in Cuzco Quechua, and contrasts with that observed for English must. Apparently marks that the speaker’s evidence for the proposition is indirect and supposedly marks that the speaker has reported evidence for the proposition and that they distrust the report. In addition to what evidentials mean, this dissertation considers what speakers use evidentials to do. I show that speakers use evidentials to negotiate the common ground (cg) of discourse. While a bare assertion proposes its propositional content for inclusion in the cg, speakers use actually-assertions both to propose the propositional content for inclusion and to advocate for its inclusion by marking that the speaker has best evidence for that content. Because actually highlights the strength of the speaker’s evidence, it can be used to achieve delicate discourse actions like correcting, challenging and disagreeing. In questions, actually puts the addressee on notice that the information proposed in a bare assertion cannot be included in the cg without more information; actually-questions encourage the addressee to justify their evidence either by disclosing the source of their evidence or by expressly aligning as author and/or principal of that information. Speakers use apparently and supposedly to proffer information that may be relevant to the investigation but without proposing it for inclusion in the cg, because they are either agnostic about its reliability or know it to be untrustworthy.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".