Bibliographic record
Abstract
This book analyzes education reform through the eyes of those entrenched in the process—policy makers, administrators, middle managers, principals, and teachers—in the context of care. A senior administrator, who participated in the implementation of an unprecedented series of reforms that flattened the education system in a Canadian province and rebuilt it with a new mandate, examines learning from the shortcomings of the past and provides a critical enquiry that can help determine the success or failure of future reform efforts by shedding light on the obstacles to avoid, problems to correct, and methods to embrace in order to overcome hurt and disappointment in a turbulent environment and foster more caring and effective educational organizations.Few attempts have been made to write a book about women’s work from the perspective of those in senior leadership roles in education; others have written about it but not experienced it firsthand. This book illuminates the controversial debate between women and gender in education and challenges assumptions about equity and the caring and democratic nature of education. It contributes to a broader understanding and knowledge of the complexities of leadership work within education, which in turn can lead to improvement in professional relationships as well as organizational effectiveness. The book contains enlightening and compelling stories about the unique and shared experiences of people navigating turbulence within an organization.Author Mary Green draws on her career spent teaching and learning to provide a unique Canadian perspective and context. She offers a rigorous self, social, historical, and political reflection of educators, who despite experiencing particular challenges, draw purpose from faith in the possibilities and potential of more caring practice in education. The content will prove useful to those committed to infusing more humanity into work in education with reference to individuals, institutions, and the social and political challenges in the field. Specifically, this book is relevant to graduate students in faculties of education, policy makers, principals, other administrators, and organizational leaders. Universal issues of power and politics reveal interconnections between the personal and the global workplace, underscoring the importance of care in the workplace.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.022 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".