Bibliographic record
Abstract
Ali Meghji’s The Racialized Social System (2022) examines the study of critical race theory (CRT) as a “practical social theory” that examines the micro, meso, and macro dimensions of race across time and space. Meghji sectionizes his analysis into four chapters, the first of which adapts Pierre Bourdieu's theory of “social space” to link the unequitable distribution of resources to the social construct of racial hierarchy at the macro level. Then, in the next chapter, he explores the racialization of emotions on a micro level, detailing how dominant racial ideologies politicize emotions. In the third chapter, Meghji introduces the concept of interactional order and interactional risk(s)/right(s) as a measure of inequitable racial distribution and emotional regulation. Then, in the last chapter, Meghji examines how meso-organizational spaces (schools, workplaces, industries, etc.) are designed to restrict the agency of racialized individuals, while giving agency to their white counterparts. Through this multileveled approach, Meghji illustrates how the “practical social theory” of CRT is a viable tool for analyzing and understanding racism both theoretically and practically. [1] Micro (small) and macro (large) are intertwined “through the racialized social system approach – offers a framework which links the macro structure of racial hierarchy to the micro workings of everyday life, emotions and perceptions” (2022, p.55). Meso (middle) – Meghji refers to “those in-between organizational spaces – education, workplaces, industries and so on – that are worthy of analysis in their own right” (2022, p.90).
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".