Political Apologies and their Challenges in Achieving Justice for Indigenous Peoples in Australia and Canada
Bibliographic record
Abstract
In the last 25 years we have witnessed the rise of official apologies. These apologies can advance national reconciliation and justice for victims. They also require the introduction of other reparative measures to overcome the practical effects of past injustices and ensure against their repetition. Unfortunately, however, this is not how these apologies usually work in practice. As the article argues, state apologies function in a paradoxical way. In making apologies states seek to acknowledge and accept responsibility for past wrongs; at the same time, states use them to limit their liability. The apologies made in Australia and Canada to Indigenous peoples in 2008 will be examined in view of this analysis. Ultimately, the article argues that while these apologies seem to be addressing past wrongs, they have done little to change the status quo. In advancing these claims, the article emphasizes the importance of history to the apology-making process. En los últimos 25 años ha aumentado el número de peticiones de perdón oficiales. Estas disculpas pueden promover la reconciliación nacional y la justicia para las víctimas. También es necesario introducir otras medidas reparadoras para superar las consecuencias prácticas de injusticias pasadas y evitar su repetición. Lamentablemente sin embargo, no es así como habitualmente funcionan en la práctica estas peticiones de perdón. Como se defiende en el artículo, las disculpas estatales funcionan de forma paradójica. Al pedir perdón, los Estados buscan reconocer y aceptar su responsabilidad por errores del pasado; pero al mismo tiempo, buscan limitar su responsabilidad. A partir de este análisis, se estudiarán las peticiones de perdón hechas en Australia y Canadá a los pueblos indígenas en 2008. Finalmente, se argumenta que aunque estas disculpas parecen estar abordando los errores del pasado, han hecho poco para cambiar el statu quo. Al plantear estas cuestiones pendientes, se hace hincapié en la importancia de la historia en el proceso de petición de perdón. DOWNLOAD THIS PAPER FROM SSRN: https://ssrn.com/abstract=2960259
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".