‘Be careful!’ Using our words as a discursive exploration of early childhood educators regulating children’s play
Bibliographic record
Abstract
What early childhood educators say to children on playgrounds shapes children’s behaviour. ‘Be careful!’ may be the most common phrase uttered on playgrounds across Ontario, as early childhood educators manage children’s play and work to maintain their safety. These utterances, and the disciplinary practices that accompany them, shape what is acceptable and unacceptable for children to do and, ultimately, what kind of children they can be. Making use of analytic strategies derived from Foucault, I take some first steps to show that injunctions to ‘be careful’ and other similar utterances regulate children’s behaviour to produce a particular child-subject, while in the same moment revealing much about some of the discourses at work in the playgrounds of many early learning settings. I propose that these discourses – the discourses of safety, socialization and purposeful play, all embedded within an overarching developmental discursive framework – connect early childhood educators’ utterances and practices on playgrounds to concepts of discipline and governmentality. I also explore in this article how a Foucauldian perspective may provide educators a space to question established ideas regarding children and their play and explore new approaches for ensuring children’s safety without controlling them.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.055 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".