Bibliographic record
Abstract
Abstract In Chapter 6, ‘Responsibility, Structural Injustice, and Settler Colonialism’, Catherine Lu focuses on what kind of responsibility is generated by structural injustice and how responsibility for structural injustice relates to the responsibility for other sorts of injustices. By way of answering these questions, Lu offers a range of examples, one of which is the heightened vulnerability of Indigenous women to violence in settler–colonial societies such as in Canada and the United States. Here she discusses the case of an Indigenous woman Mary Johns, who was murdered in 1982; the case later became a focal point in Canada’s National Inquiry into Missing and Murdered Indigenous Women and Girls in 2017. Lu argues that, while punishing individual perpetrators for wrongful conduct is of course important, it is always insufficient for addressing the structural background conditions that systematically render Indigenous women vulnerable to violence and other forms of oppression. Rather, there needs to be acknowledgement that the victims of structural injustice constitute larger categories of persons. Such categories of persons, often characterized by race, class, and gender, are subjected to social positions of structural inferiority, marginalization, and disadvantage, and this is particularly perpetrated by systemic features of states. Accordingly, Lu calls on the state and its role within the international order to take up responsibility for its contributions to the social, economic, and political structures that produce settler–colonial structures of power that render so many racialized groups vulnerable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".