Joint implicit alignment work of interaction designers and software developers
Bibliographic record
Abstract
Collaboration is an important aspect of software creation work. In field studies of 8 teams in the early stages of novel project work at 8 organizations we focused on understanding collaborative work from the perspective of both the interaction designer and the developer. We found designer-developer collaborations, often occurring in the context of team collaborations, were extensive. While some collaborations were directed towards explicit alignment work, such as prioritizing tasks, we have studied implicit alignment work, which constitutes a larger part of the overall alignment work. The form of this work varied in some respects, but in general designer-developer interactions directed towards implicit alignment were remarkably similar. Our model shows how implicit alignment work is jointly achieved; we derived it from an extensive analysis of videos of 13 collaborative events, and verified it with our observation notes and interviews. The model is applicable to a wide variety of software creation settings, including agile and non-agile teams. Our analysis shows the implications of our observations of implicit alignment work, and we conclude organizations should take practical steps to support it, as is frequently done for explicit alignment work.
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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 | 0.025 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| 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".