From Two-Eyed to Three-Eyed Seeing: A Third Space Beyond Binaries
Bibliographic record
Abstract
As a Métis therapist and academic, it is not unusual for me to write from the margins. I live on land that is referred to, by Indigenous people, as (northern) Turtle Island, aka Canada. Referring to this land by one of its Indigenous names means that we situate this space apart from the dominant, British and French colonized society. We project our Indigenous inner landscape (ways of knowing and being) onto the landscape and fortify our Indigenous inner world with reinforcing experiences of interacting with the social and natural world. As Métis people, I believe we try to also call forth a Métis space in which we can dwell, a virtual “road allowance”. In this space, we can laugh and cry together, scheme, strategize, and grieve (Richardson, 2006; Troupe and Gaudet, 2024). This article explores the “third space” and what it means for Métis people to live across multiple spaces and to resist notions of “pure race” and other forms of colonial claptrap.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 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".