The typology of self‐managed teams based upon team climate: examining stability and change in typologies
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
Several studies have reported on psychometric and factor‐analytic work related to the team climate inventory (TCI), including its four scales and 13 subscales. This exploratory study reports on the first research to examine team typologies based on team climate scores. The TCI was administered twice to 84 and 63 self‐managed teams of management undergraduates completing graded term projects. Following each TCI administration, a two‐stage clustering procedure (Ward’s and K‐means) was used to determine the number and team composition of clusters. Clusters were then plotted on the multidimensional space (INDSCAL) and a discriminant analysis performed to determine how well cluster membership was predicted using scores from the 13 TCI subscales. At the first TCI administration, three weeks into the term projects, the study obtained three team clusters on a two‐dimensional space. Near the end of the term projects, the study found two team clusters on a two‐dimensional space. Implications are presented for team building, interventions to improve team climate and management training.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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