Anti-racism in community learning disabilities services: The work for clinical psychologists. The perspective of two undergraduate black and brown placement students
Bibliographic record
Abstract
In December 2020, The North Thames Learning Disabilities Special Interest Group (SIG) met over video to discuss how Clinical Psychologists and aspiring psychologists experience, manage and combat racism in their work. This paper focuses on two presentations from the day by Islington Learning Disabilities Partnership. The first presentation focused on the barriers faced by people with Learning Disabilities (PWLD) to understand racism and receive support as victims or perpetrators. The talk identified a clear need for further accessible resources about racism and strategies to unlearn racism that go beyond cultural appreciation. The second presentation focused on the experiences of a black and a brown undergraduate psychologist joining an all-white LD psychology team in October 2020. Live semi-structured interviews were completed, informed by Whyte’s (2019) concept of robust vulnerability. The purpose was to explore different perspectives and experiences around what anti-racism means and what work that needs to be done by clinical psychologists individually and as a collective. It is the responsibility of the profession to not only diversify but ensure the needs of victims of racism are put before white discomfort and that black and ethnic minorities are not burdened with implementing anti-racist practices. Easy read summary The Learning Disabilities Special Interest Group (SIG) met over video in December 2020. The group talked about how racism affects the Learning Disabilities (LD) community. Racism is the unfair treatment of people based on their skin colour. This paper talks about two presentations from the day by Islington Learning Disabilities Partnership (ILDP). The first presentation was about support for people with learning disabilities. People are not always given support to cope with racism or to understand how to stop it. The second presentation was about anti-racism. Anti-racism means doing things to understand and fight racism. A black and a brown female student psychologist talked about their experiences in a team with all white psychologists. They interviewed two white psychologists about what they know and do to be anti-racist. They then interviewed each other about their experiences. They said that talking about racism helps people to cope with it and to stop it.
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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 | 0.027 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.084 | 0.038 |
| Scholarly communication | 0.029 | 0.011 |
| Open science | 0.007 | 0.032 |
| Research integrity | 0.016 | 0.036 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".