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Record W2116900394 · doi:10.5539/elt.v4n2p198

Check This One out: Analyzing Slang Usage among Iranian Male and Female Teenagers

2011· article· en· W2116900394 on OpenAlexvenueno aff
Sara Hashemi Shahraki, Abbass Eslami Rasekh

Bibliographic record

VenueEnglish Language Teaching · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsSlangPsychologyTest (biology)Style (visual arts)WishPhenomenonIdentity (music)Social psychologyDevelopmental psychologyLinguisticsSociology

Abstract

fetched live from OpenAlex

Slang usage in modern age Iran is a popular phenomenon among most male and female teenagers. How pervasive this variation of language use is among various age and sex groups in Iran has been a question of debate given the significance of religion in a theological system of social structure. The work presented in this study aims to investigate the effect of age and sex on variability of slang usage. Sixty Iranian participants were selected, and then were divided into three age groups (i.e. primary school, high school, and senior university students) each group consisting of ten males and ten females. A self-made questionnaire in the form of Discourse Completion Test (DCT) describing nine situations of friendly conversations was given to the participants. They were asked to make their choice on the responses, which ranged from formal to very informal style (common teenage slang expressions), or to write down what they wish to say under each circumstance. The results of the chi–square tests indicated that slang usage among high school students is more frequent as compared with other age groups. Unlike the popular belief suggesting that slang is used by boys rather than girls, the findings suggested that young Iranians both male and female use slang as a badge of identity showing their attachment to the social group they wish to be identified with.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.057
GPT teacher head0.367
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2011
Admission routes1
Has abstractyes

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