Adapting the Scrum Framework to the Needs of Virtual Teams of Game Developers with Multi-site Members
Bibliographic record
Abstract
ABSTRACT. As we all know, most video games are developed using the well-known Scrum framework as the core approach for managing the project. Indeed, Scrum is an effective value-driven approach allowing adjustments based on regular and repeated feedback. Nowadays, in the video games industry, it is frequent to see virtual teams, with fragmented groups of people, working remotely from multiple sites. However, the Scrum framework is not designed to fits the needs of a fragmented multi-site team. Scrum was designed for small teams where all members are supposed to work together on the same location, having frequent face-to-face contacts in, ideally, the same open space. When applied on a virtual team, the Scrum framework is less effective because it does not offer sufficient tools and artefacts for addressing the specific problems emerging from the virtual context, such as the communication problem, the isolation of people of the groups, the challenge of keeping transparency, etc. We propose, in this paper, a comprehensive extension of the Scrum framework allowing to alleviate some of these issues. We also present the results of a first experiment with two virtual teams developing video games.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".