Bibliographic record
Abstract
A significant amount of material exists on the topic of building and managing high performance teams. Works by Deming, Lencioni, Maxwell and others provide insight into the psychology of how teams function and all suggest strategies for raising team performance to higher levels of efficiency. However, beyond the challenge of moving the team to perform better, there is a significant amount of work required to create the team in the first instance. At some point in most managers' careers, they will find themselves in the position of taking over an existing group of employees or having to integrate several groups of employees into one functional area. The task of melding the employees and functional groups into a team and from there into a high performance team requires strategies that encompass all aspects of management know-how. This paper examines the requirements for turning functional groups into high performing teams. Various strategies are presented for building bridges that help promote trust among the team members, reduce the fear of conflict, and gain commitment. The use of standardized individual and group evaluation exercises is discussed. All of the strategies are presented in the form of a case study format that also includes pitfalls that have been encountered along the way. The paper concludes with a bridging diagram that will assist in making the transition as effective as possible.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".