Bibliographic record
Abstract
Since the early days of the 20th century, schools and educators have been subjected to numerous calls for improvement of their performance. A curious aspect of this phenomenon is that school-based educators (teachers and school administrators alike) have usually been positioned as objects to be manipulated and controlled rather than as professional creators of a learning culture. In recent years, however, this position has lost considerable credibility because school-based educators are exactly the people who deal directly with the learning of children. From that standpoint, scholars and change agents have begun to advance the notion of the learning community as a preferred strategy for school improvement. The metaphor of the learning community assumes, first, that schools are expected to facilitate the learning of all individuals, and, second, that educators are ideally positioned to address fundamental issues and concerns in relation to learning. Within this metaphor, school people are central to questions of educational practice, change, and improvement; they are the ones charged with the tasks of identifying and confronting the problems and mysteries of professional practices. But simply charging them with this responsibility will not necessarily bring about the types of profound improvement that are envisioned within a learning community. Instead, capacity for a learning community needs deliberately and explicitly to be built among educators and within schools and school systems. In this paper, we present a model that frames our understandings about the ways in which people can construct a learning community. The model consists of three pivotal capacities that we believe need to be built if a school is to function as a learning community: personal capacity, interpersonal capacity, and organizational capacity. In a recently published book (Mitchell & Sackney, 2000), we provided a fuller development of the model and we embedded it in data from several studies and projects that we have undertaken over the past decade. In this paper, we present a summarized version of the model and of our foundational assumptions.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.009 | 0.021 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".