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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".