Bibliographic record
Abstract
I offer some tentative thoughts on Sto:lo relations with the forest and, in turn, suggest how those relations may inform Sto:lo views on identity and culture. While highlighting the variety and complexity of Sto:lo attitudes toward the forest, I pay particular attention to those that appear contradictory. In so doing, I suggest that such "contradictions" are instead necessary antagonisms that spring from the constantly changing pressures to which the Sto:lo have been subjected, as well as from the ways in which they have struggled to cope with such pressures. More specifically, in pointing to Sto:lo attitudes towards forest work and forest conservation, I suggest that the Sto:lo have been forced and even encouraged to make claims to their identity that do not, and need not, conform with what is considered "traditional." In this way, my discussion is structured around the relationship between a sense of Sto:lo identity and the notion of cultural continuity, while aimed at highlighting the material as well as the intellectual realities behind that relationship. In a broader context, my discussion is aimed at reinforcing the need for more flexible examinations of Native identity; those that will highlight what it means to live in a modern Native culture, and what it means to be vulnerable to power.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".