Attitudes toward Affirmative Action Programs: A Q Methodological Study
Bibliographic record
Abstract
This study examined the structure and content of attitudes toward affirmative action programs, including preferential hiring based on gender or minority group status. Ninety-seven individuals recruited from the community (51 women, 43 men, 3 of unspecified gender), were presented with 70 statements obtained in a telephone survey of attitudes toward affirmative action programs. They sorted the statements on an 11-point scale ranging from -5 (least like my point of view) to +5 (most like my point of view). The Q sorts were factor analyzed using principal components analysis with varimax rotation. Three interpretable factors emerged. Factor 1 was defined by 15 women and 28 men. The group expressed strong negative reactions to affirmative action programs, focusing mainly on qualifications and merit of candidates. Factor 2 was defined by 22 women and 6 men. In contrast to the first group, participants on this factor were in favor of affirmative action programs, a position that appeared to be based on recognition of inequality in the work place and the need for change. Finally, Factor 3 was defined by 7 women and 6 men, whose attitudes seemed to be based primarily on the denial of disadvantage. Despite the fact that affirmative action policies have been in effect for as long as 30 years, only a relatively small proportion of respondents appeared to understand the need for and goals of these policies. Results of this research provide new insights and a basis for work to change misconceptions about affirmative action. Comparisons between a single-item attitude measure and the 3 perspectives represented in this study help to illustrate the usefulness of Q methodology in subjective studies.
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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 | 0.062 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".