Bibliographic record
Abstract
Racial discrimination is a matter of public health and social justice and an issue that lies at the very heart of the social work profession. Modern forms of racial discrimination are frequently hidden, subtle, and unintended. This type of discrimination, described by the construct of racial microaggression, poses significant challenges to social work practitioners, educators, and researchers striving to promote justice and equality. The construct, however, also offers a powerful tool for understanding and intervening in discrimination. This paper defines and traces recent developments related to the concept of racial microaggression and discusses how acts of microaggression perpetuate prejudice and oppression. The tenets of Critical Race Theory, in which the construct of microaggression is grounded, is presented with a discussion for why postracial discourse may be counterproductive toward efforts aimed at deconstructing and eliminating racism. The paper concludes with specific recommendations for how the social work profession can integrate knowledge about microaggression into practice, policy, education, research, and intervention in a way that avoids potential pitfalls associated with addressing this sensitive issue.
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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.020 | 0.073 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".