Missing and Murdered Indigenous Women and Girls National Inquiry:
Bibliographic record
Abstract
In this paper we present a rhetorical genre analysis of the Missing and Murdered Indigenous Women and Girls (MMIWG) National Inquiry. We focus on the concepts of meta-genre and genre hybridity in the context of social change to explore the dynamics of the MMIWG Inquiry as an instantiation of the “truth commission” (TC). Following Giltrow (2002), we treat meta-genre as advice and criticism from genre participants about how a genre should be performed. We apply Gready’s analysis (2011) of the TC as a hybrid genre that has emerged in the context of transitional justice and post-modern governance: the hybrid incorporates three sub-genres: the state (public/national) inquiry, the human rights report, and the official history (rewritten and archived). Our goals are to examine what the concepts of meta-genre and genre hybridity offer to help explain the difficulties of national inquiries/truth commissions in general, and specifically to help illuminate the problematics of the MMIWG Inquiry. Our qualitative analysis focuses on public and media metageneric commentary on the MMIWG Inquiry, including the Commissioners’ responses, in both mainstream traditional media and social media. Our findings show that meta-generic commentary on the MMIWG Inquiry falls into five main categories or themes, each deriving from stakeholders’ expectations raised by the tributary genres. By far, the most dominant theme is criticism of the Inquiry for its recolonizing legal framework: the ideology of colonialism that inhabits the TC’s state inquiry tributary genre is the object of significant meta-generic criticism. The other four recurrent themes are the perception that the Inquiry should be a criminal investigation, criticism of the Inquiry for its restriction to an “advisory” role only, calls for the inquiry to have a human rights framework, and the expectation that the inquiry is to facilitate meaningful reconciliation. We suggest that, as a recurring and constitutive feature of genre, and, as an arena of negotiation over how genre is to be performed, meta-genre can function as a kind of oversight and challenge that, as an index of social change, inhabits genre as a response to its own inertia. We also suggest that the TC genre creates genre confusion through its conflation of the widely divergent and broad exigences of its tributary genres. We conclude that, at the time of this writing, stakeholders’ diverse expectations, the TC’s problematic hybridity, and the MMIWG Inquiry’s colonizing, statist, legal framework constrain the impetus for change, rendering the Inquiry “truth-lite” (Gready, p. 50) and low impact, and affording only “thin reconciliation”.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".