(Re)making Assessment in the Trans-Systemic Space Shaped in the Meeting of Personal, Indigenous, and Relational Ways of Knowing and Being
Bibliographic record
Abstract
Inquiring into Trudy, Shaun, and Janice’s experiences alongside Anishinabe Elder Dr. Mary Isabelle Young’s living pimatisiwin (walking in a good way) and pimosayta (learning to walk together) with us, we show how her living in these relationally ethical ways grounded our creating and offering an Assessment as Pimosayta course in two Canadian teacher education programs. The authors built from Mary’s teaching to include the experiences, knowledge, perspectives, and worldviews of Indigenous community members and scholars. These beginnings shaped openings for attentiveness to relationally ethical assessment through Indigenous, holistic, narrative, and relational ways of knowing, being, doing, and relating. Learning to dwell in enduring tensionality has been central, as this tensionality has emerged in Trudy, Shaun, and Janice’s attempts to create with the teachers the trans-systemic process and spaces imagined by Battiste. Trudy, Shaun, and Janice see that the enduring tensionality experienced in this middle space opens potential to begin to live the respectful, ethical, relational, and … ecological relationships described by Donald and the resultant ethical relationality that needs to ground these relationships.
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 | 0.011 | 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 | 0.013 | 0.038 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".