Finding a Voice for Child Participants within Doctoral Research: Experiences from the Field
Bibliographic record
Abstract
Grown-ups never understand anything for themselves, and it is tiresome for children to be always and forever explaining things to them. -de Saint-Exupéry, A. (2000) CHILDREN HAVE A LONG HISTORY of being the object of study across several research disciplines (psychology, education, sociology). Typically, studies on children have tended to assess concepts, experimental approaches, theories or interventions with little consideration given to children's voices within that research (Hill, Laybourn, & Borland, 1996; Scott, 2008). Increasingly, within the various research communities, there has been a shift toward conducting research with children rather than on children to gain insight into what is meaningful and significant to children themselves (Clark, 2005; Clark 2007). As such, several scholars acknowledge four perspectives in conducting research on children: child as object, child as subject, child as social actor, and child as participant or co-participant (Alderson, 2008; Christensen & Prout, 2002; Christensen & James, 2008). Yet, despite recent research trends of including children's perspectives, many academic disciplines are steeped in a traditional approach of perceiving the child as object, a dependent and vulnerable being in need of protection. This article discusses the methodological choices and tensions I experienced as an early childhood education doctoral student as I endeavoured to negotiate a participatory role for young children's voices within a research agenda that focused on sibling teasing (Harwood, 2008a).
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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 | 0.057 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.039 | 0.034 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.005 | 0.045 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".