Decolonial process tracing: Indigenous rights and pipeline resistance movements
Bibliographic record
Abstract
This article explores how decolonial methodologies and Anishinaabe gkendaasowin (ways of knowing) can augment detailed narrative process tracing methodologies used to examine social and political processes. While detailed narrative is most frequently used as a tool of causal inference, focusing on the unfolding of a singular time, I see potential for it to be enriched by Indigenous legal traditions that emphasize epistemic diversity and multiple temporalities. Analyzing how Indigenous rights are leveraged in decision-making processes for the Line 9 and Line 3 pipelines, I show how a decolonial approach to process tracing (DPT) that centers Anishinaabe gkendaasowin can change both the actors and power relations involved. Recognizing that energy decision-making processes take place alongside, outside, and within colonial state institutions, and are embedded in the land as constellations of reciprocal kinship responsibilities, DPT opens space to examine two kinds of Indigenous rights: those acquired through struggle with state institutions, and those inherent to Indigenous communities’ attachment to place. DPT addresses the shortcomings of a focus on linearity by privileging inherent rights that are often excluded from detailed narrative process tracing. To take inherent rights seriously, one must also take more-than-linearity and more-than-humans seriously—and DPT is uniquely positioned to do this. The key features I propose for decolonial process tracing are grounded constellations, multiversality, and multitemporalities. Decolonizing methodologies and Anishinaabeg studies provide direction for more expansive, decolonial process tracing techniques which can in turn help understand the relationship between temporalities, law, and energy governance.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".