Effects of agile practices on social factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Programmers are living in an age of accelerated change. State of the art technology that was employed to facilitate projects a few years ago are typically obsolete today. Presently, there are requirements for higher quality software with less tolerance for errors, produced in compressed timelines with fewer people. Therefore, project success is more elusive than ever and is contingent upon many key aspects. One of the most crucial aspects is social factors. These social factors, such as knowledge sharing. motivation, and customer collaboration, can be addressed through agile practices. This paper will demonstrate two successful industrial software projects which are different in all aspects; however, both still apply agile practices to address social factors. The readers will see how agile practices in both projects were adapted to fit each unique team environment. The paper will also provide lessons learned and recommendations based on retrospective reviews and observations. These recommendations can lead to an improved chance of success in a software development project.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it