Implementing along a Continuum: Comparing the Embedded Agency of Leaders and the Coupling Orientation of Educational Systems
Bibliographic record
Abstract
Purpose: In the wake of interlocking pandemics, educational systems are resetting in important ways, pursuing aims that are not exclusively instructional (e.g., social justice, well-being). As systems (re)build, they may vary in how they manage their institutional and policy environments, which may or may not be reorienting toward these same expanded aims and at a similar pace. Drawing on theoretical lenses related to coupling and the embedded agency of leaders, this article compares the work of system and school leaders as they reconcile system-level policies with their environments, and with teaching and learning in classrooms. Research Methods/Approach: Drawing on data from four elementary schools, situated across two educational systems (i.e., Montessori and International Baccalaureate) and two national contexts (i.e., United States and Canada), I highlight differences—between and within systems—in implementation of system-level policies. Findings: I find an educational system’s “coupling orientation” provides context for the embedded agency of leaders and for the nature of their work as implementation agents. By orienting its leaders toward a certain coupling arrangement between macro (i.e., environment), meso (i.e., system), and micro (i.e., classroom) levels, a system can shape the type of work leaders do, and the degree of structure and agency they experience. Implications: In detailing differences in implementation between and within systems, this research helps scholars frame loose and tight coupling, structure and agency, along a continuum. For educational leaders, this article highlights necessary system knowledge for managing interactions between the environment, their system, and teaching and learning in classrooms.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".