Signs of Dysconscious Racism and Xenophobiaism in Knowledge Production and the Formation of Academic Researchers: A National Study
Bibliographic record
Abstract
Abstract The relation of social ethics to knowledge production is explored through a study about academic research enquiry on minoritised and racialised populations. Despite social change related to migration and ethnicity being a feature of contemporary Northern Ireland, local dynamics and actors seemed under-studied by its research-intensive ‘anchor universities’. To explore this, a critical discourse analysis of published research outputs (n = 200) and related authors’ narratives (n = 32) are interpreted within this paper through conceptualisations of consciousness. Insiders’ perspectives on the influences and structures of the research journey demonstrate the ways in which research cultures (mis)shape the politics of representation, authorship and ethicality. Societal and political disregard for the new publics, reproduced within universities’ hidden curriculum, has been negotiated and to some extent resisted in the research practices of those marginalised (such as women academics), those entering the system (migrant academics), and those local-born whose referential frames were developed external to local universities. Of concern is that the few research enablers were characterised by techno-rationality and doublespeak, impoverishing the depth of theorisation, complexity and intellectual debate necessary for challenging the existing dysconscious racism and xenophobiaism of the social imaginary.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".