Applying Agar’s Concept of ‘Languaculture’ to Explain Asian Students’ Experiences in the Australian Tertiary Context
Bibliographic record
Abstract
This paper reports part of a broader qualitative case study of Asian students “translation” (Agar, 2006) to study in an Australian university. The paper is concerned with the experiences of eight participants and their involvement in a training programme in the use of language learning strategies (LLS) to support their engagement with second language (L2) academic and social discourses. Agar’s (1994) concept of languaculture is used to frame the study. The participants’ ability to translate between languaculture 1 (LC1—their home linguistic and cultural context) and languaculture 2 (LC2—the linguistic and cultural context of Australia) is investigated. The findings indicate that LLS can be assistive in this process but that there are contextual and linguistic factors that mediate success. These findings, and the data from the study, have enabled a refinement of Agar’s (1994; 2006) languaculture model to better accommodate how training in the use of LLS can support translation from LC1 to LC2.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".