Researching Racism: The Colour of Face Value, Challenges and Opportunities
Bibliographic record
Abstract
Abstract Researchers and practitioners in social work value qualitative research for the opportunity to engage with issues of social justice including relations of power, and attention to the political, historical and social relations of difference. Interview narratives are all too often accepted at face value as authentic, true voice, representing experience without analysis of what is being represented politically. An analysis of the relations and operations of power provides additional contextual insight to face-value analyses with further opportunities for understanding and social change. When left uninterrogated, face-value analyses are permeable to the reproduction of knowledge without critical analyses of race, ability, sexual orientation or gender and can perpetuate modernist ideas that knowledge is observable and transparent and (re)institutes Western/Eurocentric knowledge as dominant/superior. This paper explores critical reflections on our research and provides a discussion of some of the opportunities identified from our research experiences. Through a discussion of the representation of voice as a production in progress; an attention to analyses for historical, social and political positioning; and a critique of face-value analyses, a conceptual framework is offered that may assist researchers to resist reliance on or accepting of analyses as transparent that eludes an analysis of racism and other forms of discrimination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.118 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.018 | 0.083 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.005 | 0.008 |
| 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".