Bibliographic record
Abstract
In this article, I argue that political solidarity is important to justice. At its core, political solidarity is a relational concept. To be in a relation of political solidarity is to be in a relation of connection or unity with one’s fellow citizens. I argue that citizens of a shared state can be said to stand in such a relation when they have attitudes of collective identification, mutual respect, mutual trust, loyalty and mutual support toward one another. I argue that there are distinctly social bases for political solidarity and that justice requires that social and political institutions be structured so that they promote these. As an example of the sort of social and political arrangements that might encourage relations of political solidarity, I discuss Canada’s response to the recent H1N1 pandemic and the failings of this response with respect to members of the Aboriginal community within Canada. I focus on these issues because considerations relating to solidarity and justice have received little attention in most discussions of them. I argue that ensuring that members of the Aboriginal community had equal access to the goods they needed to protect themselves against H1N1 infection would have more effectively promoted political solidarity and, in turn, would have more effectively promoted justice.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.070 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".