Say Ça: \n \nA Case Study of Experiential Learning by Volunteers through a Tutoring and Mentoring Program for Adolescent Syrian Refugee Students in Montreal
Bibliographic record
Abstract
This thesis presents a case study of the volunteers working at Say Ça to contribute to the literature on volunteering as community-based experiential-learning. Say Ça was established and funded through a grant obtained by a small group of Fulbright scholarship holders studying at Montreal universities. It is a non-profit organisation offering weekly free language tutoring and mentoring sessions and monthly cultural outings for Syrian refugee youth ages 12-18. It is at present in its third phase. This thesis project focused on the lessons learnt by five out of 13 volunteers of the first phase including the author’s personal insights as a volunteer and participant observer. The two main research questions were: What lessons did the volunteers learn and how did this experiential-learning impact their previous knowledge in teaching English as a Second Language (ESL) and/or French as a Second Language (FSL) and other areas of their life. The data were collected as narratives from the five participant volunteers derived from one-on-one audio-taped interviews and the autoethnographic narrative of the author. The lessons learnt in response to the research questions were many and they resonate through these stories and are grouped under these three headings: Understanding the general needs of refugee youth; the image of the ideal volunteer for similar programs and the importance of effective communication and continuous feedback with volunteers. Further research directions emerge for a deeper examination of ethical mentoring as well as the importance of helping volunteers by developing their skills for empathy and cultural sensitivity.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".