Bibliographic record
Abstract
Purpose: Researchers and scholars have called for greater attention to collaboration among and between educational leaders in districtwide reform. This work underlines the important social aspect of such collaboration and further investigates the type of professional interaction among/between district and school leaders particularly around the Common Core State Standards (CCSS) and characterizes such interaction by key factors. Research Method: The work takes place in one school district of more than 30 schools serving students from traditionally marginalized backgrounds. Descriptive statistics, multilevel social network modeling, and network sociograms are used to understand the characteristics of professional interactions around CCSS implementation among district and site leaders. Findings: The findings indicate similarities and differences in characteristics of leaders who likely seek CCSS advice and leaders who likely provide that CCSS advice. Leader self-efficacy in implementing the CCSS positively explains the likelihood of both seeking and providing advice behaviors, and yet other factors (organizational learning, leadership, job satisfaction, and CCSS beliefs) each makes different contributions to the likelihood of seeking and/or providing the CCSS advice. Conclusion and Implications: This work suggests a discrepancy of leaders’ perceptions between advice seekers and providers, signaling a need for closing the perception gap between advice seekers and providers such that the leadership team could better craft coherent norms of collaboration in instructional improvement. Understanding the “why” of CCSS advice ties may help guide leaders toward the “how” to align professional and social aspects of change.
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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 | 0.003 | 0.026 |
| 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 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".