Critical Consciousness is an Individual Difference: A Test of Measurement Equivalence in American, Ukrainian, and Iranian Universities
Bibliographic record
Abstract
We live in a world in which we are socially, politically, economically, and environmentally connected with other people. Online communication has facilitated people coming together from different parts of the world. In terms of social justice movements, people have come together to share ideas about how they perceive social inequality and how to address it, which is what academics call critical consciousness. While scholars have explored critical consciousness in the American context, whether it operates on a global scale is under-explored. To address this question, we administered the Critical Consciousness Scale (a validated survey) with students from the United States, Iran, and Ukraine. Our findings demonstrate that critical consciousness maintains its factor structure across the entire sample, meaning that students from these three countries share some notions of critical consciousness. However, when comparing national groups, we find that critical consciousness is defined differently by students in different countries. In a practical sense, these findings mean that some aspects of critical consciousness are shared, but there are important differences in how it is perceived and how its components relate to one another. By attempting to understand critical consciousness internationally, this study serves as a cautionary narrative for international solidarity movements organized around the goal of social justice.
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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 | 0.018 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".