Functions of Fate Schema: The Case of Persian and English
Bibliographic record
Abstract
With an aim to contribute to the present knowledge of intercultural communications, this study explores the cultural schemas of ghesmat and fate in Persian and English societies. Particularly, it tries to demonstrate how comparable these two cultural schemas are across the speakers of the two speech communities. Data were collected by triangulation, through ethnographic observations, movies, and websites. The collected data were analyzed qualitatively and quantitatively. To perform a quantitative analysis the frequency of the schema of fate and ghesmat in different situations in English and Persian was calculated. Furthermore, to find out the underlying themes of the schemas activation a qualitative content analysis and Wierzbicka’s semantic analysis were employed. The findings further improve the cultural knowledge involved in intercultural communications and discuss the sociocultural roles of ghesmat and fate cultural schemas in the speech communities. Key words : Schema; Cultural Schema; Ghesmat; Fate; Cultural Keywords; Intercultural Communication Resume: Dans le but de contribuer a la connaissance actuelle des communications interculturelles, cette etude explore les schemas culturels de ghesmat et le destin dans les societes persane et anglaise. En particulier, il essaie de montrer a quel point ces deux schemas culturels sont comparables a travers les locuteurs de ces deux communautes linguistiques. Les donnees ont ete recueillies par une triangulation, c'est-a-dire a travers des observations ethnographiques, des films et des sites web. Les donnees recueillies ont ete analysees qualitativement et quantitativement. Pour effectuer une analyse quantitative, la frequence du schema du destin et de ghesmat dans de differentes situations en anglais et en persan a ete calculee. D'ailleurs, afin de trouver des themes sous-jacents de l'activation des schemas, une analyse qualitative de contenu et l'analyse semantique de Wierzbicka ont ete employees. Les resultats ameliorent davantage les connaissances culturelles impliquees dans les communications interculturelles et discutent des roles socioculturels de ghesmat et des schemas de destin culturels dans les communautes de la parole. Mots-cles: Schema; Schema Culturel; Ghesmat; Destin; Mots-Cles Culturels; Communication Interculturelle
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".