Families, Efficiency, Gender, and Justice
Bibliographic record
Abstract
Book panel commentary on Joe Heath's "The Efficient Society" from the May 2002 Canadian Philosophical Association meeting in Toronto Ontario: I'm going to focus my comments on a relatively small part of Joe Heath's book, the section on the household division of labour. Although it's a small piece of a much larger picture, I've chosen this area for two reasons: First, it connects with my own interests in issues of family justice. Second, I think for me it highlights a potentially larger problem concerning the relationship between justice and efficiency. When Heath puts the contrast between those who place rights before efficiency in terms of a contrast between the US and Canadian health care systems, I find that I'm in agreement with the argument for efficiency. But when I think about the contrast in terms of the question of the gendered division of domestic labour, I'm less certain that I want to accord efficiency the kind of status it has according to Heath. (I must confess that I've always thought of Canada as the just society, rather than the efficient society. If it turns out that it's both I don't think I'll mind but if we were to come out as efficient but unjust, then I'm prepared to let efficiency go.) Maybe there's also a 3rd reason and that's that as a mother of 3 who is about to become chair of her department I spend a lot of time being asked and answering the question, how did you do it? I'm not a single parent so there's no great mystery here. What many people, mostly women, mean is how did you get your husband to accept a division of household responsibilities that allowed you to do this? Heath's chapter on efficiency and the home helped me articulate the answer. In terms of education I had trained up and married down.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.016 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".