Bibliographic record
Abstract
When we speak of historic injustice and the need for redress of those injustices, we tend to speak about land. After all, so the common narrative goes, what was taken from the Indigenous nations was land, and so to redress past wrongs, land must be returned to present day Indigenous people. In this essay, I argue that talking about land as the sole, or even as the primary form of redress misses the point because while settler governments did in fact organize a wholesale theft of Indigenous lands, that is not all that was taken and so is not all that needs to be returned to Indigenous nations to redress past wrongs. I make my argument within the framework of corrective justice, and I reason that the first thing you need to do in thinking about corrective justice is to identify the precise wrong that you are attempting to remedy. In the case of Indigenous nations, I argue that the single greatest wrong committed against Indigenous peoples has been the historical and ongoing suppression of institutions in Indigenous communities that positively affirm Indigenous values, cultures, and identities. The suppression of these institutions means that contemporary Indigenous people cannot flourish as Indigenous people because they do not have access to the social, cultural, and political resources that affirm their identity as Indigenous people. To redress past and present-day wrongs against Indigenous people in a framework of corrective justice is to return to Indigenous communities modern and contemporary institutions that affirm ancient Indigenous values and practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.083 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".