Ensemble mentorship as a decolonising and relational practice in Canada
Bibliographic record
Abstract
Inspired by collaborating on a shared vision of reconciliation, three authors explore ethical relationality and the practical ways in which their heterarchical ensemble mentorship serves to decolonise and advance a shared vision of reconciliation for university teaching and learning. As Indigenous and non-Indigenous educators, we are buoyed by those developing decolonising and Indigenising strategies in formerly colonised regions. Seen as a promising interruption to a neoliberal approach to education, the authors embrace the possibilities of imagining and creating an ethical space in universities where relationality is prioritised in service of social justice. While the complex nature of reconciliation within a Canadian context begets tension and highlights what are often conflicting value systems within academe, we maintain that innovations in teaching and learning are possible in what is now a globally disrupted terrain as students, faculty, administrators, and university leadership contend with the unknown, encounter collectivist Indigenous traditions, and tentatively explore decolonisation as an ethical avenue towards inclusive and empowering education. In imagining what is possible, we build upon Indigenous knowledge traditions and the work of leadership studies scholars to propose 'ensemble mentorship' between students and faculty as a collaborative and decolonising teaching and learning practice.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.017 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".