Bibliographic record
Abstract
For critical psychologists, addressing social justice is not about avoiding a negative in the discipline and profession of psychology, or about following rules in order to avoid legal or professional sanctions. “Doing no harm” in research and practice is certainly a minimum requirement, but attending to injustice must also be about caring for members of society by participating in and contributing to a just society. The term social justice has become a wide-ranging catch-all term for a variety of activities inside and outside of academia, encompassing struggles in different kinds of spheres, outside the legal system, with the assumption that justice cannot only be achieved in the courtroom, but requires questions about wealth and privilege. It is suggested to move the debate from justice to injustice, and to identify three central forms of injustice: An analysis of social injustice in the political–economic realm (redistribution) and in the intersubjective domain (recognition), and an analysis of injustices of subjectification. It is argued that critical psychologists must attend to injustices in all three domains. The fact that critical psychologists can identify blind spots when it comes to justice, and that even critical psychologists’ interest has shifted to problems of subjectification and recognition, reflects a historical trend that avoids challenging the political–economic foundation of forms of injustice. The chapter ends with a discussion of the environmental crisis as a justice issue in critical psychology. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".