Measuring Socioeconomic Status
Bibliographic record
Abstract
This study investigated issues related to commonly used socioeconomic status (SES) measures in 140 participants from three cities (Atlanta, Boston, and Toronto) in two countries (United States and Canada). Measures of SES were two from the United States (four-factor Hollingshead scale, Nakao and Treas scale) and one from Canada (Blishen, Carroll, and Moore scale). Reliability was examined both within (interrater agreement) and across (intermeasure agreement) measures. Interrater reliability and classification agreement was high for the total sample (ranger = .86 to .91), as were intermeasure correlations and classification agreement (range r = .81 to .88). The weakest agreement across measures was found when families had one wage earner who was female. Validity data for these SES measures with academic and intellectual measures also were obtained. Some support for a simplified approach to measuring SES was found. Implications of these findings for the use of SES in social and behavioral science research are discussed.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".