The Ingredients of Transformation: Towards a Theory of Free Energy in Teams
Bibliographic record
Abstract
The ecology of teams has changed in fundamental ways. Teams have to continually adapt their internal structure, processes, and activities in an increasingly dynamic environment. We develop a Theory of Free Energy in Teams to identify the ingredients of a team’s capacity to initiate change, adapting Gibb’s Free Energy from the natural sciences to team research. We introduce team social capital, team interdependence, and team emotional activation as the equivalents of enthalpy, entropy, and temperature in Gibb’s Free Energy and as the main drivers of free energy in teams. We propose that team social capital and team interdependence increase free energy, and team emotional activation enhances the effect of team interdependence on free energy in teams. We conceptualize free energy in teams as a common ingredient of transformation in teams and as a driver of team adaptation, team creativity, team proactive performance, team resilience, and reduced flux in coordination in teams. We exemplify the value of our Theory of Free Energy in Teams by discussing practical examples with different constellations of team social capital, team interdependence, and team emotional activation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".