Ethnicity and interpersonal influence : an expectation states approach
Bibliographic record
Abstract
This thesis examines the relationship between ethnicity and perceived competence within the framework of status generalization theory. The theory holds that status characteristics which are significant in the larger society (e.g., sex, age, ethnicity) come to affect expectations of performance and actual performance outputs in task oriented groups. Ethnicity was chosen as the independent variable, and "White" and "East Indian" constituted the values of the variable. The study was initiated in order to determine the effect of ethnicity (as operationalized) on the amount of influence accepted and the performance standards applied to self and other. The thesis outlines the theory and the scope conditions under which it applies. As well, evidence is provided to substantiate the claim that ethnicity is a status characteristic in Canada and that, in particular, persons of East Indian origin are considered to be of low status. The results of two related experiments are discussed, the first examining the effects of ethnicity alone, and the second examining the combined effect of ethnicity and performance on the dependent variables identified. The findings of Experiment One show some support for the prediction that East Indians are perceived as less competent than Whites. However, the effect of the variable is not as strong as predicted. As a further indication that the variable (as operationalized) lacks strength, the effects of ethnicity are eliminated with the introduction of equal and average scores in Experiment Two. Contrary to expectations, gender differences are evident in both experiments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".