"Still water, who knows you?" : counter-mapping traditional knowledge and ancestral values with Nak'azdli Whut'en
Bibliographic record
Abstract
Many nations globally are seeking reconciliation with Indigenous peoples who continue to suffer at the hands of colonialism and resource extraction. In Canada, development proponents must engage with First Nations in impact assessment processes, seemingly foregrounding Indigenous values in environmental decision making. Consultation entails traditional use and occupancy (TUO) studies: mapped inventories of subsistence activities and traditional ecological knowledge. However, First Nation communities express dissatisfaction with the way their worlds are represented in assessments: for example, criticizing TUO maps for overlooking the deeper significance of harvesting activities in favour of ‘thin’ descriptions of Indigenous interests in the land. This dissertation asks, how does such consultation capture Indigenous interests, values, and world views, and how might it do so more effectively? I draw on two years of ethnographic fieldwork with Nak’azdli Whut’en, a First Nation community in British Columbia, to trial new possibilities for characterizing landscape meanings and significance in Indigenous land-use decision-making contexts. In chapter 2, I illustrate how subsistence activities comprise richer place associations than are represented in TUO studies. I suggest that conventional TUO studies do more to support colonial acquisition of land and resources than to protect Indigenous lifeways and territories. Nevertheless, TUO data remains a useful record of communities’ spatialized knowledge of territory: chapter 3 explores two ways TUO data may be reinterpreted by Indigenous land managers to represent landscape meanings more accurately to outside audiences and to better inform Indigenous-led land use planning. Acknowledging the limitations of approaches based on ‘old maps’, in chapter 4 I employ a mapping methodology sensitive to both material and extra-material expressions of value – including local understandings of social-ecological relations – to elicit a broader range of landscape values. In this characterization the Nak’azdli landscape is alive with both human and more-than-human ancestral kin. Chapter 5 explores this reality within the context of emerging attention to ‘relational values’ within a dominant approach to land-use decision-making, the ecosystem services framework. Ultimately, I conclude that improving social-ecological justice in such decision-making contexts requires assessment approaches that are rooted in Indigenous ways of knowing and supported by Indigenous jurisdiction in traditional territories.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.023 | 0.018 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".