Bibliographic record
Abstract
This chapter narrows focus on interpolation of fairness into citizenship in three dimensions -- all of which proceed from a feminist politics of substantive equality. First, note locates an understanding of fairness in traditional social democratic espousals of social justice. Second, more specifically, this understanding runs in tandem with feminist critiques of impact of neo-liberal governance and gendered consequences of neo-liberal variant of individualization of ideal citizen. In contrast, idea of social citizen postulates a citizenship more responsive and celebratory of difference and social situatedness, recognizing the complex and unavoidable inter-dependencies that dominate contemporary human inexperience. So, this chapter is concerned with idea of social citizenship, both in counterpart to citizenship norms of neo-liberal polity and as a mechanism for reinvigorating social justice outcomes -- ones that emphasize equity, democracy, diversity and redistribution for women.The commentary also touches briefly on relevance of spatial arrangement of our cities to fairness of urban citizenship, a localized set of social relations and practices key to most Canadian's daily experiences of citizenship. The chapter argues that a positively radicalized citizenship for women -- and other marginalized groups -- must necessarily engage with spacialization of politics and inequality in our built environments. This conversation takes on and recasts ideas about public and private -- both politically and socially, but also geographically. It also offers an angle on citizenship that understands many of goods and rights envisioned by social citizenship to rest on commitments to a social equity landscape, to a fair country -- both in terms of political and physical. The length of chapter forestalls an intricate unpacking of these claims. Instead, author aims to be reflective of some existing feminist consideration of both fairness and citizenship in a provocative and progressively evocative manner.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.066 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| 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".