Bringing action research to the curriculum development process
Bibliographic record
Abstract
In this article, we describe our efforts as project coordinators to negotiate directions and meanings at the initial stages of a childcare curriculum development project for children from birth to age five in the Province of New Brunswick, Canada. Understanding curriculum as a complex relational dynamic that is shaped by the multiple social and cultural contexts in which teachers and learners dwell, and mindful of the ways in which our discursive practices simultaneously include and exclude, we accepted, as an ethical matter, our responsibility to create spaces for critical conversations in which practitioners’ voices can be heard. Reflecting on the project proposal and notes from our meetings with practitioners, it is evident that while we have been somewhat successful in making explicit our personal views on a social constructivist approach to curriculum development and presenting counterpoints to the dominant discourse of school readiness and a culture of performativity, we have, thus far, been less successful in fully engaging practitioners. As we move forward with the project we consider how we might engage more deeply with practitioners in critical conversations about curricula content and form. We consider the possibilities of emancipatory and collaborative action research with practitioners that will, in the final analysis, enable us to extend the evaluative question ‘Are the results good, for whom, and in what ways?’ to ‘Says who?’
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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 | 0.297 | 0.220 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.020 | 0.071 |
| Scholarly communication | 0.031 | 0.029 |
| Open science | 0.009 | 0.028 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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".