International Service Learning: Catalyst for transformation in language learner identity?
Bibliographic record
Abstract
An ever increasing number of North American universities offer International Service Learning (ISL) programs to meet the interests of civic minded students hoping to gain international experience and develop cultural awareness while, at the same time, “making a difference” to a community in need. While there is an abundance of anecdotal accounts to support the claims that combining study abroad experiences with service-learning pedagogy has great transformative learning potential, there is a lack of reliable evidence (Grusky, 2000). This paper will present the theoretical framework of the author’s doctoral research; a qualitative study exploring the link between cultural sensitivity and student investment in language acquisition by following Spanish language learners participating in a short term international service learning program. I focus on the transformation (Kiely, 2004) in students’ cultural sensitivity due to the interactions with host communities during the “service” portion of the ISL component and how this cultural awakening in turn transforms the students’ social identities and their investment in acquiring the target language (Norton, 1995). Grusky, S. (2000). International service learning: A critical guide from an impassioned advocate. American Behavioral Scientist, 43(5), 858-867. Kiely, R. (2004). A Chameleon with a complex: Searching for transformation in international service-learning. Michigan Journal of Community Service Learning, 10(2), 5-20. Norton Peirce, B. (1995). Social identity, investment, and language learning. TESOL Quarterly, 29(1), 9-31.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".