Creating and sustaining disadvantage: the relevance of a social exclusion framework
Bibliographic record
Abstract
Over the last decade, public home-care services for elderly people have been subject to increased rationing and changes in resource allocation. We argue that a social exclusion framework can be used to explain the impacts of current policy priorities and organisational practices. In this paper, we use the framework of social exclusion to highlight the disadvantages experienced by elderly people, particularly those who cannot afford to supplement public care with private services. We illustrate our argument by drawing on examples from previous studies with persons giving and receiving care in the province of Québec. Our focus is on seven forms of exclusion: symbolic, identity, socio-political, institutional, economic, exclusion from meaningful relations, and territorial exclusion. These illustrations suggest that policy-makers, practitioners and researchers must address the various ways in which current policy priorities can create and sustain various types of exclusion of elderly people. They also highlight the need to reconsider the current decisions made regarding the allocation of services for elderly people.
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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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.022 | 0.103 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".