A Research Agenda for Extending Agile Practices In Software Development and Additional Task Domains
Bibliographic record
Abstract
This article is intended to serve as an introduction to this special issue on agile practices. In doing so, we briefly survey a number of key issues that are emerging in the application of agile practices to software development (SWD) and, similarly, examine recent work on extending knowledge about these practices to other task domains. We note that the extant literature on agile practices has been criticized for lacking a theoretical basis and comment on various ways that a theory orientation can enhance the accumulation of knowledge in this area. We also address issues that expand our current understanding of agile practices as they apply to non-SWD tasks. We present a framework for surfacing and discussing some of these emergent issues. We comment on the articles in this special issue and situate them within the research framework. We discuss some of the topics we think are likely to become influential as agile practices move outside the SWD domain and, finally, we present some summarizing observations.
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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 | 0.054 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.024 | 0.060 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 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".