Bibliographic record
Abstract
This qualitative research study presents descriptive and explanatory multiple case analyses offering a description and analysis on relational decision making among school district leaders responding to a district wide videoconferencing policy. This exploratory study was conducted using an interpretive mixed method multiple case approach. Interviews and document analyses were the primary data sources used to collect data. Eleven rural principals, five urban principals and five district administrators were interviewed using a semi-structured interview guide. Network analysis, Bates ACTIONS model (2000) and Brazer & Keller (2006) multiple stakeholder decision making models formed the conceptual framework for the data collected. The range of documents included annual reports, board meeting minutes and policy drafting. Triangulation of the data (Patton, 2002) contributed to the validity and credibility of the data analysis. Among the leaders studied, the network formed as a know-how network of influence. The rural leaders’ network emerged as an inflexible thin network where information exchange limited network capacity. The urban and district leaders’ network emerged as a dense tightly closed network. For rural leaders, learner impact from the videoconferencing influenced decision makers most. Cost influenced urban decision makers most. The district leaders considered organizational impact as their most important decision making factor. Instructional and curriculum decisions were the top decision making task for rural leaders. Strategic resourcing was the top decision making task for urban leaders. The district leaders ranked centralized and decentralized decision making as their top ranked decision making task. Rural leaders used student learning, school process and perception data to guide their decision making with implementation. The urban leaders used solely student learning data. The district leaders used student learning and school process data. Rural leaders used type 2 and type 3 collaborative decision making style within staff meeting and school council structure. Urban leaders used type 2 and type 4 collaborative decision making style within a committee structure. District leaders used type 1, type 2 and type 4 collaborative decision making style within a committee structure when involving others in shared leadership.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".