Increasing Minority Group Participation and Influence Using a Group Support System
Bibliographic record
Abstract
Abstract Irrelevant source cues such as age, sex, or ethnicity sometimes cause people to devalue the contributions of others who are different. In this study, heterogeneous groups of Chine se and Caucasian men solved a management problem. In the groups that met face‐to‐face, where irrelevant source cues were readily observable, the Caucasian men dominated the Chinese male subjects. However, in the group‐support‐system groups, where these source cues were less visible or absent, the participation rates and influence levels of the Caucasian subjects were not significantly different from those of the Chinese subjects. Résumé L'ǎge, le genre ou l'origine ethnique peuvent affecter la perception que se fait une personne du travail d'autrui, quand ce dernier provient d'un groupe présentant des caractéristiques différentes de celles de l'évaluateur. Dans cette recherche, des groupes hétérogènes, formés de personnes d'origine chinoise et d'occidentaux (caucasiens), ont travaillé sur des problèmes de gestion. Dans les groupes qui avaient des rencontres face‐à‐face, où les différences étaient facilement observables, les sujets occidentaux ont eu une performance supérieure aux sujets chinois (participation, contrǒle du processus, etc.). Toutefois, dans les groupes utilisant le collecticiel, où ces différences étaient réduites ou měme invisibles, les niveaux de participation et d'influence des sujets occidentaux n 'étaient pas significativement différents de ceux des sujets chinois.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".