On hard work in early childhood education pedagogical inquiry research—Or, how do we do hard work while researching together?
Bibliographic record
Abstract
Drawing on public writing from a pedagogical inquiry research project collaboration between three early childhood educators, a pedagogist-researcher, and preschool-aged children, this article debates how pedagogical inquiry research becomes “hard work.” Against the backdrop of mainstream early childhood education in the lands currently known as Canada, where research is often conducted toward producing universalized best practices or contributing to the machine of child development, this article pays patient attention to rhythms, tensions, and practices of attuning that animated our research, pausing and unpacking moments that felt especially like “hard work.” Refusing to see “hard work” for its colloquial neoliberal connotations, we ask how hard work happens and how hard work makes happen. Thinking with three modes of hard work—remembering, dis/placing and re-placing, and manifesting into a commons—we share questions and encounters that crafted a character of hardness within our laboring together. Importantly, we resist naming all that might be hard work in pedagogical inquiry research, instead inviting readers to consider the situated, slippery, and continually made and re-made contours of hard work in pedagogical inquiry research.
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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 | 0.073 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.029 | 0.184 |
| Scholarly communication | 0.037 | 0.028 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".