Bibliographic record
Abstract
Hope, schools, professional learning communities,and school improvement planning – what links these words? According to Hulley and Dier (2005), hope is the key to achieving successful and effective schools through reculturing with professional learning communities as the vehicle for change in the school improvement process. Wayne Hulley, president of Canadian Effective Schools Incorporated and senior consultant for the Franklin Covey Company, has 35 years of experience in North America working in the area of school improvement. Co-author Linda Dier has extensive knowledge having worked for 30 years in education systems in Manitoba and Saskatchewan. Currently, she is senior consultant with Canadian Effective Schools Inc. and administrator of the Canadian Effective Schools League. Together, Hulley and Dier have written a text for educators and administrators at the district, board, and school levels, combining research theory with the practical knowledge gained in their joint 70+ years’ experience in education to provide a comprehensive planning process for school improvement. This text presents a step-by- step process that notes the highs and lows or « implementation dips » of the school improvement cycle. The authors have utilized the learning community model of professional development as a vehicle to facilitate, guide, direct, and sustain change towards successful and effective schools.
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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".