Bibliographic record
Abstract
Audrey Lorde stated with some regret and certainly with much anger that, within western institutional frameworks, difference always escaped conceptualisation that was not oppressive. 1 As a rightful heir to these concerns, Iris Marion Young devoted many of her writings to analysing difference in addition to arguing that proper attention to difference is crucial for a more just society.Class, social position, gender, race, ethnicity, ability and sexual orientation are all human variations that affect access to social and political institutions.While attention is fittingly directed at the challenges of the pluralist state, which imply different cultural locations, I believe there are other sources of difference which require attention: namely, age.If age is currently mediatised, attention is focused on the possible scarcity of resources, especially in health care, that will result from an increased numbers of seniors.Consequently, reflection on age and citizenship has been circumscribed to issues of distributive justice that will be brought about by this demographic change.If the practical implications of aging have gained attention, theoretical discussions of the citizen usually omit considerations of age.In traditional liberal democratic theories, the concept of the citizen is taken to be an ideal devoid of such contingent particularities.I challenge this apparent age neutrality and I support my claim by drawing on Iris Marion Young's critiques of universal citizenship and of the social institution of labour.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".