More than “petty squabbles” – Developing a contextual understanding of conflict and aggression among older women in low-income assisted living
Bibliographic record
Abstract
Dominant approaches to relational aggression among older adults tend to conceptualize the problem as a behavioral or interpersonal issue, and can inadvertently infantilize the phenomenon as 'bullying.' In this article we use a narrative approach and the conceptual lens of precarity to develop an in-depth, theoretically informed analysis of relational aggression between older women in low-income assisted living. The analysis of the narratives of tenants (and a manager) indicated that past life experiences and intersecting threats to power and identity shaped and could intensify tenants' interpretations of and reactions to others' actions and comments. Conflicts over a) unequal distributions of caring labor, b) control of social activities, and c) access to appreciation are complex and rational responses to precarious contextual conditions. Findings contribute empirically to the body of research on relational aggression among older adults, expanding this field through connecting it to critical gerontological conceptualizations of precarity. Preventing relational aggression requires increased public investment in formal social supports for older adults, challenging dominant discourses that privilege independence, and recognizing how the legacies of past disadvantage and contextual precarity (as opposed to mental illness or dementia) shape social interactions with and responses to others.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".