Strategies to Use Deceptive Statements in the Iranian Academic Context
Bibliographic record
Abstract
Lying, as a deceptive act in social interactions, has received much attention among social psychologists. Studies in this area mostly aim at developing lying taxonomies and finding verbal and non-verbal clues for detecting liars. However, it seems that the generalizability of their findings, because of cultural and contextual differences, is limited. Hence, admitting the fact that lying can be context and culture-specific, this study attempted to investigate lying among professors and students in the Iranian universities, to developa taxonomy for lying, and also to examine the role of gender and status in this relation. For this aim, frequent interactions between students, professors, and university staffs were identified and, based on these interactions, a questionnaire was designed and administered to 120 students and 80 professors. Results revealed 18 types of lies among which ‘projection’ and ‘forgetting’ were among the most frequent lies. Same-gender parings were found to tell more lies to each other. It was also found that professors and students tend to tell more lies to higher status people.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".