Pragmatic Empathy as a Grand Strategy in Business Letter Writing
Bibliographic record
Abstract
This paper examines the employment of pragmatic empathy as a grand strategy in business letter writing. To account for the realization of pragmatic empathy in business letters, we make a corpus-based manual analysis of four types of business letters. It is found that (1) the choice of deixis is a major concern in most letters: while second person is often used in competitive letters, first or third person frequently appears in other three types of letters; (2) conventionalized indirectness strategy is often used in competitive business letters while mitigation strategy is preferred in conflictive business letters; (3) the employment of different strategies is an adaptation to various empathetic concerns in business letter writing. It is concluded that different types of business letters are characterized by different pragmatic strategies to achieve empathy. This paper, which is an attempt to investigate business letters from the empathy perspective, sheds light on business discourse research in general and business letter writing in particular.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".