Linguistic Taboos: A Case Study on Australian Lebanese Speakers
Bibliographic record
Abstract
This study aims to investigate how Lebanese Arabic speakers living in Australia utilise their linguistic taboos, with the purpose of comprehending their cross-cultural adaptation in the Australian context. The specific research focal point of this study includes how and why Lebanese Arabic speakers of different age range use linguistic taboo words. A total of 56 Lebanese students were deemed to satisfy the participation criteria. A research tool, NVIVO 10 software, was used to analyse the questionnaires and interviews and to help sort major themes, as identified above, for critical discussion. The results show that the older participants tend to use a specially designed euphemistic form of linguistic taboo whereas the younger participants’ use of linguistic taboo is much influenced by some factors such as peer pressures and video games, and they do not always use more taboo words than older participants as reported in previous research. The results indicate the complicated and dynamic sociolinguistic context of the Lebanese community regarding the use of linguistic taboo in Australia. Additionally, it provides insights into how Lebanese speakers manage linguistic taboos successfully in social interactions using their cross-linguistic skills and cross-cultural knowledge.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".