Self-Verification and Self-Enhancement in Newly Formed Multicultural Groups
Bibliographic record
Abstract
The purpose of this study is to determine when self-verification (the motivation to confirm one’s self-concept) and self-enhancement (the motivation to be viewed in a positive light) occur in newly formed multicultural groups. Many suggest that enhancement is a prepotent motive to verification (Sedikides & Gregg, 2008; Sedikides, 1993), but when verification occurs, it results in greater group performance, trust, and group identification in diverse groups (Polzer, Milton, & Swann, 2002). Research on intimate relationships has shown that factors such as relationship length, as well as levels of future certainty, commitment, and evaluation can influence preferences for enhancing versus verifying feedback (Swann, De La Ronde, & Hixon, 1994; Campbell, Laackenbauer, & Muise, 2006). Based on this research, I predict that groups with a certain future, high commitment, and low evaluation, which I will designate as the “married” groups, will demonstrate higher self-verification than will groups with an uncertain future, low commitment, and high evaluation, designated as “dating” groups. These “dating” groups will demonstrate higher enhancement compared to the “married” groups. As well, as a result of greater cultural verification, the “married” groups will exhibit greater cultural mosaic beliefs (emergence of diverse cultural perspectives and better overall group performance) about their group as well as higher levels of trust, group identification, and group commitment, relative to the “dating” group. The “married” groups will also demonstrate higher performance than the “dating” groups on tasks assessing creativity and accuracy.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".