Indigenizing the Galaxy: Terristory and Kinship in Drew Hayden Taylor’s “Lost in Space”
Bibliographic record
Abstract
This article examines posthuman companionship in Indigenous Canadian writing through Ojibway author Drew Hayden Taylor’s short speculative fiction story “Lost in Space” (2016). Drawing on Métis author Warren Cariou’s concept of ‘terristory,’ I approach human-robot interactions as a process of kinship-making that rewrites the sterile confines of the spaceship to effectively ‘Indigenize’ space. I argue that the robot companion is instrumental in permitting positive affective attachments to cultural practices, thereby re-tethering the human protagonist to his Indigenous identity, even in a restrictive space that is not conducive to Indigenous ways of life. In this manner, Indigenous-robot interactions are constructed as crucial in contesting Western narratives that frame Indigeneity as inherently anachronistic, and that contribute to the protagonist’s initial isolation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".