There and Back Again: Seeking Balance in the Implementation of Post-agile Approaches
Bibliographic record
Abstract
Post-agile approaches (e.g., DevOps) that integrate development and operations have been proposed to improve the quality and time-to-market of digital products. Notwithstanding the benefits of these approaches, we still know little about the ways they are implemented to enable integration between development and operations. Accordingly, we ask the research question “How are post-agile approaches implemented, and what are the expected benefits associated with their implementation?”. Drawing from paradox theory, we conceptualize the seamless integration between development and operations as a state of perfect tension. Interviews with 23 industry professionals reveal three main perspectives on the implementation of post-agile approaches in practice: tool-centric, actor-centric, and task-centric. We also find that the ideal tension between development and operations is typically out of balance, with a focus on the operationalization of development activities. Based on these preliminary findings, our work contributes to literature on the development and operation of digital products.
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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 | 0.103 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".