Donner la parole aux jeunes et faire entendre leurs voix : défis d'une recherche auprès de jeunes d'origine haïtienne à Montréal
Bibliographic record
Abstract
Cet article s’appuie sur une recherche doctorale (Lafortune, 2012) menee aupres de jeunes Montrealais d’origine haitienne. Dans le cadre d’entretiens individuels a caractere biographique, ces derniers etaient invites a raconter leur experience socioscolaire depuis la maternelle. Il leur etait egalement demande de recommander des personnes significatives de leur environnement familial, scolaire et communautaire a meme de parler d’eux. Le point de vue de ces personnes devait permettre de degager une vue systemique de la trajectoire socioscolaire. L’approche s’est revelee tres fructueuse en raison de la richesse des informations collectees. Toutefois, elle a aussi pose des defis relatifs au traitement de la parole des jeunes, parole parfois menacee d’etre couverte par la voix des personnes significatives ou celle de la chercheuse. L’article traite de ces defis a partir d’un des cas etudies. Abstract This article is based on doctoral research (Lafortune, 2012) conducted among young native Haitians in Montreal. They were invited to share their socio-academic experience since kindergarten through in-depth biographical interviews. They were asked to designate significant persons from their family, school, and community who could also be interviewed, so that a systemic view of the participants’ socio-academic trajectory could emerge. This approach was very successful and yielded rich data. However, challenges emerged regarding how to bring out the youths’ voices in the presentation and interpretation of the interviews so that they would not be drowned out by the voices of those significant others who were also interviewed. The article discusses these challenges in light of one of the case studies.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".