Reform and community care: has de-institutionalisation delivered for people with intellectual disability?
Bibliographic record
Abstract
Reform and community care: has de-institutionalisation delivered for people with intellectual disability? In this paper we provide a post structural analysis of the theoretical shifts informing changes to service delivery over the past 150 years in relation to people with intellectual disability. We utilise the New Zealand experience of reform as it reflected global knowledge at any given period. Firstly, we address the historical modes of treatment and care, with reference to the eugenics movement, the concepts informing 'Prisons of protection' and moral treatment. Secondly the paper traces reforms commencing in the 1960s where changes from institutional care to community care were informed by humanistic ideals, a key driver being the concept of normalisation. Theorists offered competing discourses that formed the bases of arguments for the status quo whilst resistant voices advocated change. Covering such significant changes leads us to assess the state of de-institutionalisation' as it stands today and how it may be perceived in the future. We assert that Foucault's genealogical approach provides analytic tools to uncover the dynamics of changing attitudes and approaches to service delivery. In applying a Foucauldian lens to the trajectory of reforms concerning institutionalisation to de-institutionalisation we question whether a form of re-institutionalisation may be occurring.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| 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".