Examining Child-Led Tours and Child Standpoint Theory as a Methodological Approach to Mitigate Asymmetrical Adult-Child Power Dynamics in Ethnographic Research: A Child-Led Tour of Elfish Antics and Sensorial Knowledge
Bibliographic record
Abstract
Child-led tours alongside intersectional feminist theory and child standpoint theory provide promising methodological insights regarding meaningful engagement and research approaches with young children that can inform intersectoral pediatric healthcare practice and policy. However, research has paid little attention to the dynamics between children and adults during research and promising methods and theories that may mitigate asymmetrical relationships of power. The authors describe lessons learned from a child-led tour through the lens of an intersectional feminist, child standpoint theoretical orientation regarding child assent, power, and control. The strength of a child-led tour coupled with a reflexive intersectional, child standpoint theoretical orientation is that it can make explicit adult epistemological biases and the tensions between children’s and adult’s interactions and collaborations. Further, this framing may make medicalized and taken-for-granted scientific assumptions of childhood and children explicit and allow for the reimaging of children’s agency, power, and capacity for knowledge generation in situ. Child-led tours coupled with an adult researcher’s commitment to anti-oppressive practice through methodological accountability and frameworks have the promise of eliciting rich, embodied, sensorial data in pursuit of knowledge mobilization for and with children. Child-led tours as an ethnographic, qualitative interview method are proposed to be child-friendly, enabling meaningful knowledge gathering concerning children’s perspectives, ideas, and experiences. More research on the potential for a child-led tour combined with an intersectional, child standpoint praxis is needed to prevent tokenistic methodological strategies that reproduce asymmetrical power relations and dynamics.
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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 | 0.031 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.033 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".