Profound Improvement: Building Capacity for a Learning Community
Bibliographic record
Abstract
This text positions the learning community as a vehicle for professional learning and school development. The learning community develops in response to building capacity in three domains: personal, interpersonal and organizational. In the personal domain, educators deconstruct and reconstruct their professional narratives to enhance student learning and professional practice.In the interpersonal domain, educators generate norms and values that foster experimentation and critical analysis of educational practice and that promote collective and individual learning. In the organizational domain, visible and invisible structures are constructed that enable community members to enact educational practices in support of profound improvement in teaching and learning. The book focuses on the life of educators as it relates to professional learning and growth. It is concerned with human growth and development, human cognition and affect and human interactions and actions in the context of a school community. It places at the centre of the discourse some of the less controlable aspects of professional development - the undercurrents of professional presuppositions and beliefs as well as the surface waves of professional knowledge and learning. From this perspective, building a learning community is a dynamic process that engages the individual, the group and the organization in embedded interdependencies and mutual influences.
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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 | 0.000 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".