The (radical) role of belonging in shifting and expanding understandings of social inclusion for people labelled with intellectual and developmental disabilities
Bibliographic record
Abstract
There is a gap between the desired outcomes of social inclusion policy and the everyday experiences of people labelled with intellectual and developmental disabilities. Despite belonging rhetorically named in social inclusion policy and practice, belonging is often absent in the lives of people labelled with intellectual and developmental disabilities and remains undertheorised in its relationship to social inclusion. In this paper, we explore the role belonging might play in narrowing the gap between how social inclusion is theorised and how it is experienced. Drawing on critical disability and feminist relational theories, we outline a relational conceptualisation of belonging and use it to 'crip' the construct of social inclusion. Exploring the synergies and tensions that surface when social inclusion and belonging are held together as discrete but interconnected constructs, we name four conceptual shifts and expansions that allow us to see social inclusion differently. Through the centring of the experiences of people labelled with intellectual and developmental disabilities, we explore the ways belonging can help to reimagine inclusion from assimilationist, static, objective and formal towards inclusion as fluid, negotiated, (inter)subjective, (in)formal and intimate.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.114 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.004 | 0.007 |
| 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".