Professional learning as policy enactment: The primacy of professionalism
Bibliographic record
Abstract
In this article, we draw upon notions of occupational professionalism and organizational professionalism to interrogate the complex, and sometimes contradictory, teacher learning practices that characterize educational policy enactment. We apply these understandings to the Growing Success assessment and evaluation policy in Ontario, Canada, and in relation to how five educators in varied positions in a regional school district made sense of this policy through professional learning. Our research considers how the interactions between professionalism and teacher learning can be deployed to better understand policy enactment as part of educators’ work and learning. The research reveals that organizational professionalism, characterized by hierarchical modes of decision-making, standardized work practices, external regulation and accountability processes, limited teachers’ learning. However, at the same time more occupational professionalism cultivated forms of professional learning necessary for productive enactment of educational policy. In relation to teacher learning for policy enactment, more occupational professionalism was characterized by, inter alia, commitment to student learning, the generation of dialogue about teachers’ assessment practices, coherence in relation to the whole reform agenda, a focus upon the immediacy of practice, and accountability to one another. The research indicates that even as more organizational professionalism is clearly evident in policy reform, the occupational cultures fostered by districts and schools can have significant beneficial effects for how teacher learning is expressed as a form of policy enactment.
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 | 0.041 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.113 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| 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".