Violência e mulheres em quadrinhos indígenas: estórias que resistem
Bibliographic record
Abstract
This thesis aims to analyse the graphic novel Will I See? (2016) , written by two Indigenous Canadian writers, i skwé e David Alexander Robertson, from a critical theoretical perspective i nterested i n denouncing violent actions historically exerted against Indigenous women. Therefore, our goal i s to analyze how the theme of missing and murdered Indigenous women i s portrayed i n the graphic novel selected for our study; We discuss the way the publishing of comic books produced by Indigenous authors have been relevant for the discussion of such a subject i n contemporary Canada. Departing from the reading of theoretical and critical references on the subject, our purpose i s to reflect on this genre ( graphic novel ), discussing i ts characteristics, which have often been used by Indigenous people to create their l iterary representations. We also discuss violence experienced by First Nations, especially by their women l iving i n Contemporary Canada, taking i nto consideration studies questioning sexism and racism, mainly the one directed against native people. In this sense, we take studies by Joyce Green (2007), Allison Hargreaves (2017) and Paula Gunn Allen (1992) to discuss violence attached to Indigenous women. As a support for our reading of the l iterary production we are analyzing, we will take studies by Will Eisner (2008) and Gérard Genette (2009) into account, among others. Finally, our aim along this research was to collaborate with the questioning, from the perspective of post- or decolonial views, of the power relations established i n contemporary Canadian society, which keep being marked by a very selective type of violence, based on racial and gender criteria.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".