Exploring motivations of non-native Persian adult students in learning Farsi in an Iranian school in Montreal: A teacher’s observations, insights and hopes
Bibliographic record
Abstract
This study examines the motivations of non-native adult learners of Farsi in an Iranian school in Montreal, Quebec. The research is also aimed at exploring the possibility that Persian language cannot be revitalized and maintained by Iranian immigrants and their children alone, but that enthusiastic non-native Persian adult learners can play a role in achieving such a goal. Experiences and insights of six participants spanning a wide range of ages (25 – 48) have been used in this inquiry, in addition to my own observations resulting from five years of experience of teaching Farsi to foreign learners. The main emphasis of my study has been examining the narratives of the six participants, listening to their ideas, motivations and hopes for learning, reading and sometimes writing Persian, as well as socializing with the Iranian peoples in Montreal, to better understand Persian culture and traditions. \nThe study reveals that more research is needed in order to understand to what extent non-native adult learners can be agents in preserving the Persian language. Researchers should listen to learners’ voices and use their insights and experiences to better understand their potential as promoters of disappearing languages, as they cannot be kept alive in the immigrants’ host country by only the native speakers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".