Italian Canadian and Italian Australian adolescent speech: A comparative analysis
Bibliographic record
Abstract
This paper reports on two separate studies in Canada and Australia on adolescent speech varieties used by high school students studying Italian as a foreign language. The focus is on examining Italian Australian speech used as the social dialect spoken by Italian Australian in certain social contexts. Students pursuing Italian studies as a second language (L2) in a local high school in Melbourne, Australia, completed a voluntary written questionnaire. The analysis of the data collected reveals patterns of adolescent communication of clique-coded language discourses. This pattern was used as the basis for cross-cultural comparisons between Italian Canadian and Italian Australian adolescent discourse. The paper provides some contextual background to the Canadiana and Australian immigration experiences, with comments on the study of Italian a L2 in both countries. This is followed by a discussion of a framework for analyising adolescent speech, with a framework that focuses on clique-coded discourse. The data is then analysed and discussed with a focus on clique-coded discourse. The paper concludes by acknowledging that Italian Canadian and Italian Australian adolescent speech reflects the types of observations suggested in the literature by researchers such as Aulino (2005), Clivio & Danesi (2000) and Danesi (2003a, 2003b); who are among the very few who have carried out cross-cultural comparisons, that is a manifestation of similarities in a distinct and recognizable speech code. The findings of these studies may have pedagogical implications in the context of curriculum content.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 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".