Understanding Collaboration in the Context of Loosely- and Tightly-Coupled Complex Adaptive Systems
Bibliographic record
Abstract
Many of the technological and social systems our society has come to depend on can be classified as complex adaptive systems (CAS). These systems are made of many individual parts that self-organize to respond and adapt to changing outside and inside influences affecting the system and its actors. These CAS can be placed on a spectrum ranging from loosely- to tightly-coupled, depending on the degree of interrelatedness and interdependence between system components. This research has explored how the process of collaboration occurs in both a loosely- and tightly-coupled setting using one exemplar of each system. The loosely-coupled exemplar related to disaster risk reduction in two Canadian communities while the tightly-coupled one involved the implementation of a surgical information management system in a Canadian hospital. A list of core elements of collaboration that should be considered essential to the success of all collaborative endeavours was developed as a result: Engagement, Communication, Leadership, Role Clarity, Awareness, Time, and Technical Skills and Knowledge. Based on observing how the core elements of collaboration interacted with one another within each of these example systems, two models were created to represent their relationships. A list of considerations that collaborative tool designers should consider was also developed and the implications of these considerations were discussed. As businesses and other organizations increasingly incorporate team-based work models, they will come to depend more heavily on technology-based solutions to support collaboration. By incorporating collaborative technologies that properly support the activity of these teams—based on the specific type of complex adaptive system in which their organization exists—organizations can avoid wasting time and resources developing tools that hinder collaboration.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".