A Theory of Shared Understanding for Software Organizations
Bibliographic record
Abstract
Effective coordination and communication are essential to the success of software organizations, but their study to date has been impaired by theoretical confusion and fragmentation. I articulate a theory that argues that the members of software organizations face a constant struggle to share and negotiate an understanding of their goals, plans, status, and context. This struggle lies at the heart of their coordination and communication problems. The theory proposes an analysis of organizational strategies based on four attributes of interaction that foster the development of shared understanding: synchrony, proximity, proportionality, and maturity. Organizations that have values, structures, and practices which facilitate these qualities find it easier to coordinate and communicate effectively.\n\nThis argument has serious implications for traditional concepts in our literature. Project lifecycle processes and documentation are poor substitutes for informal but unscalable coordination and communication mechanisms. Practices and tools are valuable to the extent that they enable the development of shared understanding across our criteria. Co-location and group cohesion take advantage of the four criteria and therefore have direct advantages for software teams. Finally, growth is detrimental to the effectiveness of the organization because it hinders the use of small-scale mechanisms and it leads to an undesirable formalization.\n\nThe theory is supported with empirical evidence collected from five case studies of a wide variety of software organizations, and it has explanatory and predictive power. The thesis links this theory to other current research efforts and shows that it complements and enhances them by providing a more solid theoretical foundation and by reclaiming the relevance of synchronous, proximate, proportionate, and mature interactions in software organizations.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".