Bibliographic record
Abstract
In Agile software development, key artefacts used to support the process are the User Story (usually recorded on a Storycard) and Story Cardwall (usually a dedicated portion of a wall).These low-delity tools work together to help teams stay focused and self-manage their projects.The need to support distributed teams and team members makes the physical Cardwall impractical and teams are therefore migrating towards digital story management tools.We wanted to learn how to design a digital Cardwall that leverages the benets of the physical Cardwall, while adding more value with features only possible in a software system.We conducted eld studies of Agile teams and performed qualitative data analysis to understand the needs for digital Cardwalls.We then used these ndings to identify guidelines for future design.vii thesis examination committee.Their insightful comments, fair questions and feedback helped improve the nal version of this thesis.A special thanks to my supervisor, Dr. Robert Biddle who encouraged me to start on this academic path and who never lost faith even when things were not progressing as expected.Finally, I'd like to thank Dr. Judith Brown, a
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 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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".