Imaginative distance: reconsidering young children's playful social language
Bibliographic record
Abstract
Traditionally, research about young children has been shaped by developmental approaches which persist in framing them as incomplete adults.This dissertation proffers a relatively new image of childhood that celebrates the possibilities inherent in children's multiple ways of knowing.It is drawn from a 2006 study of the playful social language of, and interviews with, grade one children attending an urban Canadian school.Two questions drive this inquiry: a.) What is the significance of children's social language in a primary classroom?b.) What is the role of play within children's social language and within their culture?To maintain a sense of children as collaborators in research and to bring children's talk into mainstream education discourse, Bakhtinian concepts of dialogicity and responsivity are foregrounded.The dissertation begins with a literature review that relates extant theory, research, and praxis to the study of language, discourse, and play.Then, participants' perceptions of play, as articulated in the interviews, are presented.Because the study focuses upon children's ability to make sense of their lived experience, their perceptions of play guide subsequent interpretations.Theory is reconsidered, and interpretative analysis is presented as dialogic response to the children's ways of knowing, as points of contact between texts, as dialogue.Vignettes, drawn from videotapes of the participants' social language in class, provide concrete examples of the role of play within the children's local culture.Three key ideas emerge: children are able, dialogic interpreters of their lived experience and research participants in their own right; play discourse is agentive behaviour; and iii agentive play discourse is children's response to problematic life experiences, for example, the world's gendered texts.This study illustrates how children's playful social talk places an imaginative distance between them and entrenched assumptions about what counts as knowledge.And, it challenges readers to distance themselves from the way things are, to redefine what is considered to be legitimate classroom conversation, and to reconsider how, together, children discursively make meaning and imagine themselves as social actors.iv
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".