“Do I Have to Sign My Real Name?” Ethical and Methodological Challenges in Multilingual Research with Adult SLIFE Learning French as a Second Language
Bibliographic record
Abstract
In 2017, Quebec’s Auditor General reported several major issues regarding government-funded French as a second language (FSL) courses, especially those intended for adult students with limited or interrupted formal education (SLIFE). To this day, no official framework or program exists for this specific population, a situation that the government of Quebec wishes to resolve. Our research team was thereby mandated by the Ministry of Immigration to conduct a large-scale multilingual study with the objective of gaining a better understanding of the realities and needs of the various stakeholders involved in low-literate FSL classes. We met 42 teachers, 24 French learning center directors, and 10 pedagogical advisors in individual interviews; we also led 107 group interviews with SLIFE in 26 languages, allowing us to meet 464 adult SLIFE enrolled in low-literate FSL classes from 11 regions of the province of Quebec, most of them being refugees. This article reports on the decision-making process in which we engaged to overcome the ethical and methodological challenges we faced at various stages of the data collection with SLIFE participants: recruitment, informed consent, confidentiality, interview protocol design, instrument piloting, data collection, and data translation and transcription. To make informed decisions, we had to turn to literature outside SLA (i.e., refugee research and translation/interpreting literature) for guidance. In this article we discuss the limitations and contributions of our research to guide researchers who will conduct studies with similar non-academic samples/populations.
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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 | 0.272 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.029 | 0.025 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".