E‐improvisation: collaborative groupware technology expands the reach and effectiveness of organizational improvisation
Bibliographic record
Abstract
Abstract With today's increasing pace of change, managers who are struggling to continuously adapt and survive are turning to an emerging management technique known as organizational improvisation. This field of management science draws from a metaphor based in improvisational theatre and jazz music and is defined as: The ability to spontaneously recombine knowledge, processes and structure in real time, resulting in creative problem solving that is grounded in the realities of the moment. As part of these changes, organizations are working across great distances and in groups that include diverse constituents such as suppliers, partners and customers. The distance separating these team members poses a problem for improvisation as improvisation relies heavily on interpersonal communication between group members. The collaborative wealth of creativity, innovation and productivity flows in part from this real‐time interaction. The increasing distance between group members hampers the effective reach of organizational improvisation. The proposed concept of e‐improvisation suggests that the adoption of groupware collaborative software, in particular a peer‐to‐peer offering called Groove, can extend the reach of improvisation and enhance its effectiveness. Copyright © 2002 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".