Ways to create ethical spaces to enhance learning for Indigenous students: A participatory photography study
Bibliographic record
Abstract
Our SoTL research was conducted at the Taylor Family Digital Library, University of Calgary, a Canadian research-intensive university. As librarians and educators, we wondered how Indigenous students perceived learning in library spaces. In collaboration with Indigenous students, our co-researchers and study participants, we redefined the research question: How can ethical spaces be created to enhance learning in a good way for Indigenous learners at the University of Calgary? Ermine (2007) defines ethical space as “a space between the Indigenous and Western thought worlds” to meet and work together to build engaging and enduring partnerships (p. 194). Using Photovoice and photo-elicitation methods, we collected students’ stories, then used NVivo software to analyze and interpret student interviews, photos, and discussions. Data illuminated the ways Indigenous students imagined ethical spaces for learning. These data align with the vision identified in the University’s Indigenous Strategy ii’ taa’poh’to’p: Ways of knowing (teaching, learning and research), ways of doing (policies, procedures and practice), ways of connecting (relationships, partnerships, connections to land and place) and ways of being (identity, inclusivity, leadership and engagement) (2017, p. 6) Addressing the conference’s theme of Context Matters, one of Felton’s SoTL principles, “grounded in context”, informed this work (2008, p. 122). A scoping review identified the global literature on library services and resources supporting Indigenous students’ learning, and the local context focused on Indigenous students’ perspectives of learning in informal spaces on our campus through participatory photography methods Our study provided Indigenous students an opportunity to explore ways of creating ethical spaces to support their learning, reflect, and contribute their knowledge. To move forward, recommendations and conclusions will serve to develop a plan for action. References Ermine, W. (2007). The ethical space of engagement. Indigenous Law Journal. 2007; 6(1):193–203. Felton, P. (2008). Principles of good practice in SoTL, Teaching and Learning Inquiry, 1(1), 121-125. https://doi.org/10.20343/teachlearninqu.1.1.121 University of Calgary (2017). ii’ taa’poh’to’p, Indigenous Strategy https://www.ucalgary.ca/live-uc-ucalgary-site/sites/default/files/teams/136/Indigenous%20Strategy_Publication_digital_Sep2019.pdf
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".