Do My Contributions Matter? The Influence of Imputed Expertise on Member Involvement and Self-Evaluations in the Work Group
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Exploiting the diversity of expertise in a work team is a critical factor in maximizing group performance. This article attempts to assess several sources of influence on group member perceptions regarding the value of their input to the group as well as the level of member involvement in group activity. The participants selected for this study were 216 university students (108 men, 108 women) who were randomly assigned to 36 mixed-gender groups. Groups were required to generate a negotiation strategy for two business-related cases. Measures of individual interaction styles were provided by expert judges who viewed videotapes of the group discussions and observed member behavior. Participants completed questionnaires that assessed selfevaluations of their contributions to the group’s efforts. The findings of this study offer striking evidence that imputed expertise can clearly affect group member perceptions and behavior.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it