Bringing It Closer to Home: Justice in Another “American Tragedy”
Bibliographic record
Abstract
In this chapter, we develop one of the scenarios introduced in Chapter 1 – the decision to institutionalize an elderly relative – in order to illustrate the main themes we have introduced, and in particular to stress the subtle but pervasive ways in which the drive for justice can affect our lived experiences in profound and, at times, tragic ways. This is very much an American story; relevant cultural mores and institutional structures would lead to different outcomes in many other societies. However, the way in which rational actions lead to unanticipated justice-based reactions can be experienced in any country. The beginning of a common scenario occurs when elderly parents can no longer deny the inevitable fact that they are unable to meet all their needs for independent living. The increasing physical frailties and/or problems of mental functioning, together with the deterioration of their support networks will, in time, require the elderly to seek help in their daily self-sustaining activities. It is not commonly recognized in this country that the vast majority of the elderly, though they would greatly prefer to maintain their independence, turn to members of their families for this assistance, and in most cases a female member of the family becomes a primary caregiver or care manager with varying amounts of assistance from other family members (Brody, 1985).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| 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".