Bibliographic record
Abstract
The aphasics constitute an important segment of our population. Interacting with them requires special procedures. Some of the caregivers of the aphasics and some other members of society often dismiss the speech of the aphasics as irrelevant and incoherent. This attitude towards the aphasics is counter-productive, as the interlocutors as well as the aphasics get frustrated during interactions. Against this background, this paper examined interactions with three Yoruba-English bilingual aphasics, using the relevance theory, with a view to revealing the systematic ways the meanings of the utterances of aphasics can be decoded by somebody who is not present when such a speech was recorded. The paper concludes that a better way of making inferences from the discourse of aphasics is to enter their worlds of experience, show interest in their discourses, make assumptions about their ostensions. In most cases, the discourses of aphasics fulfil at least one of the Extent Conditions. This implies that the discourses of aphasics are relevant and the effort expended in processing them can be reduced if the interlocutor/analyst appropriately deplores the necessary contextual cues and clues. Key words: Yoruba-English bilingual aphasics; Extent conditions; Relevance theory; Ostensions; Caregivers
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".