Bibliographic record
Abstract
Abstract Group faultlines are hypothetical dividing lines that may split a group into subgroups based on one or more attributes. An example of a strong faultline is a group of two young female Asians and two senior male Caucasians. Members’ alignment of age, sex, and ethnicity facilitates the formation of two homogeneous subgroups. On the other hand, when a group consists of a young female Asian, a young male Caucasian, a senior female Caucasian, and a senior male Asian, the group faultline is considered weak because subgroups, regardless of how they are formed, are diverse. As a relatively new form of group compositional pattern, the group faultline is associated with subgroup formation, and these subgroups, rather than the whole group, can easily become the focus of attention. When members strive to obtain more resources and protect their subgroups, between-subgroup conflict, behavioral disintegration, lack of trust, lack of willingness to share information, and communication challenges are likely. As a result, group performance is often negatively affected, and sometimes groups may even be dissolved. These results were repeatedly found in studies of experimental groups, ad-hoc project groups, organizational teams, top management teams, global virtual teams, family businesses, international joint ventures, and strategic alliances.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.323 | 0.094 |
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".