Bibliographic record
Abstract
Abstract What do the many translations of ‘the brain’ from the domain of neuroscience offer to social work researchers? Drawing upon disability studies and critical social work, this article examines trends and tensions across ‘neuro’ writing in social work journals and summarises some commonly recommended practices. Neuroscientific discourse has undeniable cultural influence and offers distinctive forms of evidence to social workers. Social work scholars have strategically translated neuroscience findings to access greater disciplinary status, to counter neo-liberal onslaughts on public services, to communicate on inter-disciplinary teams and to address calls for ‘new’ scholarship. At the same time, many writers readily acknowledge that they use neuroscience to justify or even revive well-established social work practices and theories. A unidirectional strategy of translation across disciplines comes with inherent risks of reinforcing hierarchy, ignoring social difference and undermining the value of social work research and practice. Neurodiversity discourse offers one example of ‘neuro’ argumentation where social justice and neuroscience have intertwined and may present an opportunity for a different type of social work translation. Social workers should be prepared to engage with neuroscience but must do so in ways that consistently reinforce social justice commitments and include a wide array of perspectives.
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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 | 0.024 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.059 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".