Bibliographic record
Abstract
Most School Improvement Plans (SIPs) in education do not guide staff efforts to tackle the root causes that prevent significant performance improvement. It is unlikely student achievement will improve should these root causes remain. As a result, NCLB mandated consequences will eventually be imposed. These consequences increase pressure to address the root causes, but do not, in themselves, help schools determine or remove the root causes. SIPs generally reflect surface level discussions about the root causes of school performance issues. This then leads to plans that address symptoms rather than causes. By improving SIP quality through better determinations of the root causes of school performance issues, staff and administrative efforts will be more focused and directed in ways that have a higher likelihood of improving results. A review of education research and literature found that educators are urged to create cultures of inquiry and to determine root causes of problems to improve student achievement; however, almost no guidance actually exists that provides a school team a detailed methodology for finding these root causes. Using experience gained by facilitating the determination of root causes in schools and in industrial settings, as well as lessons from research and literature both within and outside of education, this paper provides a recommend process, and detailed protocols and tools for defining problems to be investigated and determining their root causes. The process differentiates the facilitation, protocols and tools to be employed based on the school‘s existing organizational culture and its past performance results. These recommendations also support the development of a collaborative, inquiry based culture. Additional proposals are then provided for the entire SIP process, as well as for school district central office support, based on the lessons learned from developing the recommended process, protocols and tools.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.014 |
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".