kihteyhayak pihkswestamawnan: Wisdom Keepers Will Speak for Us
Bibliographic record
Abstract
Chris Scribe’s dissertation is premised around Indigenous leadership and learning. He worked with the University of Saskatchewan Human Ethics board to redesign what constitutes relational ethics and sources of knowledge for what traditionally has constituted a “literature review.” His work is premised on the knowledge of traditional Elders and how that knowledge is “owned”, “(re)-presented” and granted source credit. Chris’ dissertation is framed through a creative Indigenous cosmology that privileges orality, experiential learning, and artistic expression. Rather than offering a written work that privileges the language of the colonizer, his work is based in an oral and video docu-story that incorporates cultural expression and symbology. In this journey, Chris articulates through dance, artistic expression, and kinship the ways in which ancestral knowledge of leadership and learning is enacted, along with how he positions himself in his responsibilities to the ancestors, to the land, and to this knowledge. His work maintains academic standards of rigour but is organized in a non-traditional format. Throughout the video, he situates himself in his social location, and also his ancestral territorial locations, from where he explains the impetus for his work on Indigenous leadership and education, its significance, and his role and space. He then explains the Indigenous cosmology within which he is working, describing it through the use of a framework that he has created using Indigenous symbolism, life cycle, and relationships to learning and leading. He discusses the ethical journey he has travelled to incorporate the appropriate protocols in which he has engaged (through the University of Saskatchewan and with Knowledge Keepers) in order to access and share knowledge from the Elders to whom he has spoken on issues of leadership and learning (that also informs his conceptualization). He speaks about how he represents his learnings on educational leadership and his role (and others’ responsibilities) for using this knowledge to inform leadership practice. The final portion of the docu-story includes the overlaying of Indigenous leadership story-work over the “text” of a treaty document in order to represent the juxtapositions of understandings at play in the relationship between colonizers and Indigenous peoples that has impacted constructions of learning and leadership for Indigenous peoples. The final analysis culminates in an “accounting” of his research and learning on a buffalo robe.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".