Efficiency of Threats in Interpersonal Communication
Bibliographic record
Abstract
It is common knowledge that threats are typically motivated by a desire to strike fear in others. Fear appeals have received much attention in various disciplines over the last six decades and these studies have collectively garnered comprehensive results. Still, several inadequacies remain. One of neglected areas in the field of threatening communication is the lack of research on fear appeal themes in interpersonal communication. Few researchers have addressed the problem of analyzing the content of fear appeal. The paper broadens current knowledge of “threat content—threat response” correlation. To this end, firstly, threats are analyzed from a theoretical perspective to reveal their dimensions and function in communication. Then contents of threatening interactions are analyzed and statistically examined in terms of response efficacy. To this purpose, responses to threats are extracted and subsequently classified in order to find out whether addressees’ responses indicate any tendency about the outcome of an interaction. The implications drawn from this study allow us to consider how appeal to certain types of fear influences the efficiency of threatening messages.
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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 | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".