Evoking Emotions and Eliciting Heart-Felt Responses Through Exclamatives: Unravelling the Potential of aiyyoo in the English Language
Bibliographic record
Abstract
The English language is replete with exclamative utterances which convey an array of emotions and evoke strong sentiments that create a lasting impact in the hearts and minds of the users and listeners alike. However, they are not considered as conforming to the regular speech acts of the English language. The entry of aiyyoo, an expression of South Indian origin into the Oxford English Dictionary (OED), propels us to re-visit the Standard and Modified theories of speech acts and to look with fresh eyes at the striking features and contrasting perspectives on exclamatives. This paper dwells on the versatile dimensions of aiyyoo and gathers insights about its unique illocutionary force through the analysis of (1) religious texts, (2) a passage of Indian Writing in English (IWE), (3) some columns in leading English dailies and (4) typical oral Tamil discourses. The various modes of analysis serve to affirm the immense semantic potential of the exclamative. Pragmatically and stylistically, it plays a vital role in our speech acts as it helps to articulate our deepest thoughts and heartfelt emotions. Given the persuasive quality of the exclamative in speech and writing, the usage of aiyyoo should be encouraged to enhance interpersonal communication in the global community.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".