Bibliographic record
Abstract
In recent years there has been an increasing interest in the concept of citizenship as a lens through which to understand dementia practice. This move from an individualist, personhood-based approach towards an understanding of people with dementia as a group facing social and structural discrimination parallels, in some ways, that previously seen in the realms of disability and mental health which have sought to politicize those experiences. In so doing, the debate has sought to reconfigure power relations, insisting that members of such discriminated groups are people with power entitled to the same from life as everyone else. Much of the discussion to date has, understandably, focused on the larger issues of social inclusion, rights and responsibilities - reflecting the traditional concern of citizenship of individuals' relationship to the state or the society in which they live. More recently, there has been a move to conceptualising citizenship as a practice - something that is realised through action and in relationship - rather than a status bestowed. In this paper, we seek to contribute to the discussion by introducing the concepts of midi- and micro-citizenship, taken from organisation studies, as a further means by which to link the personal and the political, and as grounds to build citizenship-alliances between people with dementia living in long-term care (LTC) facilities and front-line dementia care staff. We will then seek to illustrate the usefulness of these concepts in understanding citizenship in practice in LTC facilities through analysis of data drawn from focus groups involving LTC staff, and interviews with family carers whose relatives live in LTC facilities. In conclusion, we will explore some of the possibilities that such an approach holds for dementia care practice.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".