The Politics Of Tek In Oil And Gas: Knowledge (re)constructions And Assimilation
Bibliographic record
Abstract
Indigenous Knowledge, now known by some as Traditional Ecological Knowledge (TEK), has informed Indigenous ways of life since time immemorial. Relatively recently, it has become of interest to dominant, settler society in Canada. The way in which I situate this research aims to examine TEK studies’ institutional processes, lived experiences of those processes, and finally how and why TEK is being collected and used for natural resource management in recognizing Indigenous Knowledge and reconciling a hegemonic relationship. The importance of this research is evident not only due to an increasing interest in TEK by dominant society, but also in terms of what it represents to Indigenous peoples versus how it is being defined, collected, and constructed by settler colonial state institutions to facilitate capital gain through resource exploitation. Through a socio-historical and contemporary analysis of colonization in Western Canada and the role oil and gas plays in the culture of liberal capitalism and knowledge development, TEK can be unpacked and understood in the context of settler colonial relations and structures. The methodologies employed for this research include a review of relevant literature as well as interviews with individuals who have experience contributing and collecting TEK for oil and gas development. This research suggests that TEK is inadequately understood and collected by industry and state institutions, used to appease regulatory requirements, avoiding legal battles with Indigenous communities through what industry and government understands as ‘regulatory certainty’. In this way, the state has failed in attempts to recognize Indigenous Knowledge systems and continues to oppress, manipulate, and exploit Indigenous peoples and lands.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.025 | 0.081 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".