Psychological Defensiveness as a Mechanism Explaining the Relationship Between Low Socioeconomic Status and Religiosity
Bibliographic record
Abstract
Abstract People who are low in socioeconomic status (SES) are more likely to be religious than their higher status counterparts; however, little research has tested the mechanisms for this relationship. Using data from 90 diverse societies and multilevel path analysis, we replicated findings that individuals low in SES are more religious and furthermore found that in wealthy countries this relationship was mediated by a measure of psychological defensiveness even while controlling for participants' sense of financial insecurities. These results suggest that religious belief may play a psychologically protective role for low SES individuals, independent of realistic economic concerns. Notes 1List of regions and sample sizes in alphabetical order: Albania (n = 1,999), Algeria (n = 1,282), Andorra (n = 1,003), Argentina (n = 2,359), Armenia (n = 2,000), Australia (n = 3,469), Azerbaijan (n = 2,002), Bangladesh (n = 3,025), Belarus (n = 2,092), Bosnia and Herzegovina (n = 2,400), Brazil (n = 2,649), Bulgaria (n = 2,073), Burkina Faso (n = 1,534), Canada (n = 4,095), Chile (n = 3,200), China (n = 2,015), Colombia (n = 9,050), Croatia (n = 1,003), Cyprus (n = 1,050), Czech Republic (n = 1,147), Dominican Republic (n = 417), Egypt (n = 6,051), El Salvador (n = 1,254), Estonia (n = 1,021), Ethiopia (n = 1,500), Finland (n = 2,001), France (n = 1,001), Georgia (n = 3,508), Germany (n = 4,090), Ghana (n = 1,534), Great Britain (n = 1,041) Guatemala (n = 1,000), Hong Kong (n = 1,252), Hungary (n = 1,000), Iceland (n = 968), India (n = 6,043), Indonesia (n = 3,019), Iran (n = 5,199), Iraq (n = 5,026), Ireland (n = 1,012), Israel (n = 1,199), Italy (n = 1,012), Japan (n = 2,458), Jordan (n = 1,223), Kyrgyzstan (n = 1,043), Latvia (n = 1,200), Lithuania (n = 1,009), Luxembourg (n = 1,211), Macedonia (n = 2,050), Malaysia (n = 1,201), Mali (n = 1,534), Mexico (n = 5,459), Moldova (n = 3,038), Morocco (n = 3,464), Netherlands (n = 1,050), New Zealand (n = 2,155), Nigeria (n = 4,018), Norway (n = 2,152), Pakistan (n = 2,733), Peru (n = 4,212), Philippines (n = 1,200), Poland (n = 1,000), Puerto Rico (n = 1,884), Romania (n = 3,015), Russian Federation (n = 4,073), Rwanda (n = 1,507), Saudi Arabia (n = 1,502), Serbia (n = 1,220), Serbia and Montenegro (n = 3,780), Singapore (n = 1,512), Slovakia (n = 1,331), Slovenia (n = 1,037), South Africa (n = 8,923), South Korea (n = 2,400), Spain (n = 4,820), Sweden (n = 2,012), Switzerland (n = 2,453), Taiwan (n = 2,007), Tanzania (n = 1,171), Thailand (n = 1,534), Trinidad and Tobago (n = 1,002), Turkey (n = 7,860), Uganda (n = 1,002), Ukraine (n = 3,811), United States (n = 3,991), Uruguay (n = 2,000), Venezuela (n = 2,400), Vietnam (n = 2,495), Zambia (n = 1,500), Zimbabwe (n = 1,002). 2The data can also be conceptualized as three levels, with participants on the first level, waves on the second level, and countries on the third level. Unfortunately, the sample size on the second level is too small to effectively estimate these values (second level n ranges 1–3). To address this problem we analyzed the data using country-wave combinations as the level-2 unit of analysis, and the results produced identical conclusions as those reported here.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".