Newcomer youth: How can drama education influence language acquisition, community building, and self-development?
Bibliographic record
Abstract
In Canada, newcomer youth face many barriers including difficulties with language acquisition and a lack of acceptance by peers. These barriers can impede a youth's social, emotional, and academic development (Kilbride & Anisef, 2001). According to Tajfel and Turner's (1986) 'social identity theory', an individual's self-image derives from the social categories to which they perceive themselves belonging to (Worchel & Austin, 1986). In this study, investigators use the 'social identity theory' framework to investigate how drama education methodologies can influence various aspects of one's social identity. They look at a cohort of 9 newcomer youth between the ages of 14 and 18 and their responses to a drama education program which focused on the theme of 'resilience'. The investigators studied the youth's responses to the program in relation to three variables: language acquisition, community, and self-identity. The drama education program was provided by social enterprise Drama as a Second Language (DSL) in partnership with the New Canadians' Centre of Excellence Inc. (NCCE). The research intervention included: ten drama education workshops focused on the theme of 'resilience', and a theatrical performance collaboratively devised by the youth participants. The investigators used qualitative methods to acquire the youth's responses to the intervention. Pre-test interviews and post-test interviews were conducted with the youth participants, program facilitators, and service providers. Although the final results of the study have not been examined, the investigators of this study hypothesize that youth participants will express a correlation between drama education methodologies and social identity outcomes, as well as a correlation between drama education methodologies and language acquisition development. This research is an original interpretation of known material in the drama education field. However, further investigation regarding drama education and language acquisition with a focus directly on objective skills should be conducted in the future. Lastly, further investigation of how drama education can influence changes in newcomer youth's social identity should be conducted.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".