Amplifying voices, dismantling silences: Ethical praxis in multimodal child-centered research
Bibliographic record
Abstract
Research involving children and youth, particularly those from culturally sensitive and vulnerable backgrounds, presents intricate ethical, methodological, and epistemological challenges. While acknowledging parents’ legitimate protective role, unjustified parental gatekeeping often regulates access to minors’ participation and can create tensions between adult-centric authority and minors’ evolving capacity for agency. These ambiguities get further obscured when minors’ self-identified experiences and fluid identities—especially in multilingual and culturally diverse contexts—diverge from parental assumptions or hegemonic discourses surrounding identity and autonomy. In this paper, I critically interrogate these tensions, foreground the limitations of procedural ethics when faced with emergent obstacles, and advocate for adaptive, participant-centered frameworks rooted in dialogic engagement. Drawing on three research projects that I conducted in Canada, I demonstrate how child-engaging, multimodal methodologies can facilitate semiosis and empower minors to articulate their lived realities while safeguarding their emotional safety and agency. In this regard, intersectional reflexivity emerges as a constitutive and vital framework, allowing me to address power asymmetries and ethical dilemmas with situational responsivity. This paper reconceptualizes consent as an iterative and relational process rather than a static obligation by foregrounding minors’ narratives and respecting their welfare. Ultimately, the study contributes to advancing ethically robust and transformative research practices that amplify marginalized voices, dismantle systemic inequities, foster nuanced, inclusive engagements with vulnerable participants, and challenge dominant epistemological hierarchies and centering minors’ perspectives as constitutive of broader social discourse.
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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 | 0.108 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.132 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.005 | 0.034 |
| Research integrity | 0.006 | 0.011 |
| 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".