Against the Grain: Socially Just Social Science from the Standpoint of Roxana Ng (Review Essay)
Bibliographic record
Abstract
This contribution seeks to highlight the important scholarship of Roxana Ng, arguably one of Canadian sociology and political economy’s most underappreciated theorists. Like her activism, Ng’s academic work is both wide-ranging yet firmly focused on major, unjust inequalities. Her research particularly concerns the Canadian capitalist political economy but inevitably, given the embeddedness of these social relations within worldwide historical relations, stretches beyond national borders. In particular, Ng sought to unpack the everyday, intertwined – exploitative and unjust – relations of class, race, and gender, and the ways these unjust relations are articulated through migration and citizenship. This contribution situates the reception and uneven uptake of Ng’s varied work before critically analysing her contributions to understanding (1) immigrant women’s labour in Canada, (2) the complex racialized, gendered relations of power in the academy, and (3) the liberatory potential of embodied epistemologies, specifically Qi Gong meditation. In the conclusions, I consider the overall contributions and some contradictions of her work, in moving from the local to the global, and from the personal to the political.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| 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".