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Record W2186966476 · doi:10.53841/bpsecp.2014.31.1.48

Children describing the world: Mixed-method research by child practitioners developing an intergenerational dialogue

2014· article· en· W2186966476 on OpenAlexaboutno aff
Ainslie Yardley

Bibliographic record

VenueEducational and Child Psychology · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsnot available
Fundersnot available
KeywordsUnderpinningNarrativeSpace (punctuation)PedagogyPsychologyMedical educationSociologyMedicineEngineering

Abstract

fetched live from OpenAlex

Children are becoming increasingly engaged in the practice of research, either as active collaborators with adults or as independent researchers in their own right. This paper explores aspects of training and mentoring of children engaged in research practice as independent researchers, and highlights the use of creative methodologies in mixed-method research undertaken by children. Three primary aspects of participation and training are considered in relation to the space children inhabit in the research community: the ways in which children acquire research skills and the ways in which children are mentored in their research practice; the use of creative methods as conceptual and interpretive tools in interdisciplinary mixed-method research and how creative methodologies may benefit and empower child practitioners; and thirdly the importance of dissemination of research undertaken by children, and the quality of the intergenerational dialogue emerging from it. The paper begins with a story. The story is a personal observation translated into narrative and placed here to contextualise (rather than analyse) the research work undertaken by a group of Australian children, concurrent with their counterparts in the UK and Canada, over an 18-month period between April 2011 and September 2012. It introduces a methodological framework that was the underpinning of the project designed by the children and mentored by the author.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.118
GPT teacher head0.451
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2014
Admission routes1
Has abstractyes

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