Playing across the playground: paradoxes of knowledge creation in the videogame firm
Bibliographic record
Abstract
Abstract This contribution illustrates how a videogame firm copes in managing creativity and expression of artistic values, while meeting the constraints of the economics of mass entertainment. The research is based on a case study in one of the largest video game studios in the world located in Montreal, Canada. The approach considers that the creative units of the firms are the communities of specialists (game developers, software programmers, etc.). Each of these communities, which have found a fertile soil in Montreal that nurtures their creative potential, is focused on both exploration and exploitation of a given domain of knowledge. In order to benefit from these sources of creativity, the integration forces implemented by the managers of the firm to bind the creative units together for achieving commercial successes reveal a hybrid form of project management which combines decentralized platforms with strict constraints on time, and a specific management of space that favors informal interactions. However, we suggest that the integration forces put forward by the firm are not just for harnessing creative units: they also generate creative slacks for further expansion of creativity. Copyright © 2007 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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".