Indigenous culture as an asset for student academic success : a formative mixed method case study to examine school leaders' roles in policy development, adoption and application in schools serving American Indian students
Bibliographic record
Abstract
This study explored one public school on a tribal reservation to construct understanding of leadership effect on policy supporting academic achievement. Primary data were collected between June 2011 and August 2011 providing sequential opportunities to collect School Culture Survey (SCS) (Gruenert, 1998) data and follow-up interviews with stakeholder groups. The SCS and interviews gathered data about cultural values and beliefs, patterns of behavior and relationships from multiple stakeholder groups. Secondary data included school report cards, websites, Board of Education meeting minutes, school forms, and professional development in-service training documents. The data described strengths including unity of purpose, transformational leadership, faculty collaboration, professional development, equality development, culture departmental support, the new school building, tangible assets of lands and enterprises, and intangible assets that are the people and their unique culture. Areas of concern were expectation of failure, equity measures, parent involvement, discipline, health services, a culture clash, attendance, and community infrastructure. This study brought together American scholarly expertise and indigenous scholarly expertise from the United States, New Zealand and Canada. The findings suggest a formative comprehensive systemic school improvement plan process can be developed as recommended practice for replication in other schools serving American Indian children across North America.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".