Social position and economic system justification in Canada: Implications for advancing health equity and social justice from an exploratory study of factors shaping economic system justification
Bibliographic record
Abstract
Objective: Many socio-economic reforms that could reduce health disparities are not implemented because people justify existing systems and fear changes thereto. This study aimed to identify socio-demographic factors associated with system justifying beliefs to better understand how they are maintained in Canada. In doing so, we hypothesized that (1) systems justification is a default cognitive position, buttressed by the palliative benefits of system-justification, (2) lack of success in a given system generally motivates people to doubt the legitimacy of that system, and (3) system-justifying beliefs are rejected only when the costs of doing so are low enough and/or the benefits are high enough to outweigh the innate needs-fulfillment benefits of system-justification. Methods: paid social media advertisements. Multivariable regression models identified factors associated with Economic System Justification Scale (ESJS) scores. Explanatory variables included demographic measures of social position, self-rated health, and patterns of social inclusion. Results: Among 2,619 participants, system-justifying beliefs were wide-spread, with the average level of support across ESJS scale items exceeding 50%. Lower ESJS scores were associated with worse health, more loneliness, and lower socioeconomic status. Despite the pattern that marginalization erodes system-justification, several historically marginalized characteristics (e.g., non-white ethnicity and non-binary gender) were associated with relatively high system-justification, compared to matching privileged characteristics (e.g., white ethnicity; masculine gender). Conclusion: Supporting our hypotheses, we identify a general trend that social marginalization is associated with less system-justification. Those benefitting from the status quo (e.g., healthier, wealthier, less lonely) were more likely to hold system-justifying beliefs. However, some groups who are disadvantaged within the existing system reported higher system-justification-suggesting that system oppression may be a key moderator of the effect of social position on system justification.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".