Service-Learning in the Italian Classroom: The Pedagogical Value of Student Interviews in the Italian Heritage Project
Bibliographic record
Abstract
Abstract In this article, I will describe the experiential learning project and service-learning modules that I have introduced as a core part of Italian intermediate language and Italian studies courses at St. Jerome's, University of Waterloo. Partnering with the local Italian community, and forming part of a larger Research Ethics Board–approved and SSHRC Exchange–funded project, I introduced course modules that require students to carry out interviews with elderly members of the local Italian community in relation to their immigration experience/s. The interviews were carried out in the Italian language in the case of Italian Intermediate students, while women members of the local Italian community were the targeted interviewees by students enrolled in the course Italian Women Writers. Prior to conducting the interviews, students were introduced to best practices in the compilation of oral histories, and they read and discussed key texts that expose the realities of Italian emigration from the late nineteenth century to the present. This experiential approach refers to learning activities that, in the spring and fall semesters of 2019, winter 2020, and winter 2023 involved students of both Italian-language and Italian studies courses in the process of active engagement with, and critical reflection about, lived Italian experiences.
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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".