Bridging Cultures Over-Under: Digital Navigation to Create Liminal Spaces of Possibility
Bibliographic record
Abstract
In this paper, we as educators of Indigenous students transitioning into post-secondary education, reflect on our collaborative pilot project: Bridging Cultures Over-Under, a connection of Indigenous students in similar preparation for university programs at the University of Lethbridge in Lethbridge, AB, Canada, and at Batchelor Institute in Darwin, NT, AU. Unbeknownst to the students, the story of attempted assimilation of Indigenous peoples in both countries, and the resultant socio-economic conditions, is both parallel and similar. Through Skype sessions, Indigenous students in polar opposite countries shared their own experiences, culture, history, stories, dreams and desires and some of their academic work. The goal was to understand their shared experience and further build on these relationships so they might learn from and support each other through peer mentoring. Outcomes of this project have lead to a continued connection and the development of a secure Facebook site so that the students can further build their relationships and develop a more extensive network as they continue on their academic journey.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".