The Sociolinguistics of Persian SMS: Ways to Identify Age Limits
Bibliographic record
Abstract
A kind of social system which eases communication is language. Any form of language is very important for different types of communication: interpersonal and inter-group. A form of this social system is short messaging system (SMS) or texting which has been used increasingly since 1990’s. Text messaging, as a language style, is used in everyday life to maintain social networks, to regulate events and to help entertain oneself in the open moments of one’s day. This paper thus examines the SMS style of language communication between two groups of young and middle-aged people. Thirty messages are taken randomly from 10 cell phones (five from each group). Then we analyze the effects of the writers’ characteristic (age) on message length (number of words), dialogue structure (with or without an opening and a closing), and message function (informative vs. relational) to investigate variations among these two age groups. The paper concludes that a significant difference is found between young and middle-aged texters’ linguistic properties.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".