Never Too Rich to Be middle-class: An Assessment of the Reference-group Theory and Implications for Redistributive Taxation
Bibliographic record
Abstract
Abstract Politicians of all stripes appeal to the support of the middle class and aim their policy proposals at this group. Reference-group theory explains why citizens could believe themselves to be middle class, even if their income level or social status places them above or below. It postulates that, since the reference groups of most people are relatively homogeneous, anyone could feel ‘average’ compared to the reference group. The authors aim to test this theory by comparing perceptions about the middle class with a categorisation using objective income statistics. A survey of the adult population of the Canadian province of Quebec showed a significant proportion of citizens believing to be part of the middle class, even though their equivalised income levels placed them outside of a generally recognised income range for this group. Most notably, this subjective misplacement on the income distribution was heavily concentrated among individuals whose incomes were too high to be a part of the middle class. Our results also show that support for higher taxes on the rich might be overstated, as some respondents simply do not realise that they are a part of this group.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".