Bibliographic record
Abstract
If changes that make a difference to Aboriginal learners are to be effected in public schools, then transformational change is required (Menzies, Archibald, & Smith, 2004). How is transformational change best achieved? In this article, I argue, based on a recently completed study (McGregor, 2013) that teacher learning—particularly among non-Aboriginal teachers—is critical to effecting transformation in how teachers think about Aboriginal learners as well as how they plan and deliver fully inclusive learning opportunities. After outlining a theoretical framework for transformation focused on networked, inquiry-based learning and culturally inclusive practices, I explore how one particular teacher-learning network—the Aboriginal Enhancement Schools Network (AESN) in British Columbia, Canada, offers a powerful example of how teacher learning networks can enable deep and transformational change among participating teachers and leaders. I provide exemplary stories of transformation to illustrate the power of this model to effect changes in teacher beliefs and mindsets about Aboriginal learners and culturally inclusive practices. Following this, I identify several key enabling features of the AESN, including socially just, distributed forms of leadership, relational accountability (Wilson, 2008), and affiliative, catalytic models of implementation, a focus on “new, strong and wise ways” (Halbert & Kaser, 2012, p.11) of learning, and provincial and district resources that support network learning activity. The conclusion highlights implications of this study for school jurisdictions and policy makers. Keywords: networked teacher learning; transformational change; socially just leadership
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.054 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".