The future of collaborative technology within Scrum/Agile practices
Bibliographic record
Abstract
The pandemic has caused a paradigm shift. The frameworks for how we decide what is worth preserving and reframing are on display through the adoption of collaborative technology. This literature review and evidence-based study of collaborative technology investigates the features of the technology used during COVID-19 and how those features have enabled organizations to discard/forget and preserve/remember aspects of office procedures, hierarchies, and accountability in Scrum/Agile organizational cultures. We conducted a comparative review of the most popular collaborative tools and supportive features using industry reports on collaborative technology and Scrum/Agile adoption, Google Trends, and the Factiva database to understand the levels of growth in uptake and whether usage will continue after the pandemic. Our findings suggest that technologies are being used to preserve some of the foundations of the Scrum Organizational Culture like “teamwork,” “transparency,” “honesty,” and “communication.” There is also a push to develop real-time flexible spaces for chat, notes, and meetings. A problem with collaborative tool use is that it can be difficult to maintain informal talk and the culture of knowledge sharing that develops as a result within organizations. The interest in transparency might indicate that companies might be moving away from “waterfall” methods of information dissemination and toward more collaborative features when it comes to the day-to-day task management of employees. Agile working cultures and Scrum are predominantly practiced in industries like tech or product development, so our findings only reflect the technology that is likely used in these spaces.
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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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".