Schema-Based Analysis of Gendered Self-Disclosure in Persian: Writing for Dating Context
Bibliographic record
Abstract
This paper reports a textual analysis of letters written by 21 male and 21 female participants in Persian. Each writer wrote two letters, one to a dating service and another one to a hypothetical person chosen and introduced by the center. Therefore, a total of 84 letters were collected from the participants. Schema theory was used to find the possible gendered differences between male and female self-disclosure letters. The results of this study confirmed that men and women used significantly different semantic, syntactic and parasyntactic schemata (X2 = 30.37, df = 2, p <.0001), they also wrote differently in terms of the type of schemata forming the genera of the domains. Further descriptive analysis indicated that semantic domain accounted for 74.44% of all schema types used in male letters and 78.98% of the schemata used in female letters.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".