Bibliographic record
Abstract
Tracey Deer’s most recent film, Beans (2020), examines the Oka Crisis through the eyes of Tekehentahkhwa, a young Mohawk girl who goes by the nickname “Beans.” Deer’s film is one of the few cinematic works of “la relève autochtone” that is situated entirely in the past, and by revisiting the Oka Crisis, the director examines the geographical divisions that are still in place today and discussed in twenty-first-century First Nations’ literature and cinema. By traveling to the past, Beans delves deeper into the history of these geographical divisions and their impacts. This article examines the (in)visible borders present in Tracey Deer’s Beans by discussing the geographical focus of Deer’s cinematography: a multitude of barriers between communities – and occasionally, within the same community – during the Oka Crisis. These physical borders, represented by rivers, bridges, ports, roads, and fences, as well as less tangible barriers like linguistic and ideological divisions, are often reinforced or challenged by cultural and linguistic differences which coincide with the geographical and physical divisions. These separations play a key role in Beans as the question of autonomy during the Oka Crisis and the infringement on First Nations territories has had an enduring impact on the role that geography has today in Québécois and First Nations works.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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".