From Reconciliation to ‘Idle No More’: ‘Articulation’ and Indigenous Struggle in Canada
Bibliographic record
Abstract
How do different discourses lead to changes in understandings of the world, identity, meaning and practice in Indigenous politics in Canada? This article introduces the poststructuralist theory of Ernesto Laclau and Chantal Mouffe to Canadian Indigenous studies and demonstrates that it is a unique and effective theory for understanding this question. It finds that in the last few decades, two principal discourses regarding Indigenous peoples and colonialism have circulated in the Canadian body politic—namely, (1) “reconciliation” and (2) “Idle No More.” These discourses shape the identities of both Indigenous peoples and settlers, construct understandings of the world, and determine the meaning of related political struggle, leading to real world practice and politics. The reconciliation discourse has at times been effective at becoming a dominant discourse and has often been able to constitute the meaning of important terms such as ‘decolonization.’ It serves to pacify Indigenous resistance to colonialism. Counter-hegemonic discourses on reconciliation such as ‘Idle No More’ have been able to challenge that discourse. Academic literature, newspaper articles, YouTube videos, podcasts developed by Indigenous scholars, public letters and speeches delivered by Canadian politicians are analyzed to examine the utterances and enunciations of the two discourses.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.062 | 0.043 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".