Asynchronous Ecosystem Development: Micro-mapping using scanning, ZIP-Analysis, and systemic relations
Bibliographic record
Abstract
Can systemic design optimise on realities of asynchronous ecosystem development and bridge time and space between distributed, often unrelated teams? Does it make sense to work incrementally? \n \nThis case study is based on the Canadian Business for Purpose Network (B4PN), hosted by MaRS Discovery District and funded by the McConnell Foundation. The author evaluated network activities from November 2020 to March 2022 and provided inputs to ecosystem development based on strategic clarity work. \n \nIn emergent work such as this, uneven development is to be expected. A multimethod research mindset informed the evaluation project. The interdisciplinary research draws on strategic foresight, bricolage, and social R&D and applies ZIP-Analysis and the Library of Systemic Relations to three micro maps. \n \nReflexivity raised questions about design and designers in social systems, the realities of asynchronous ecosystem development and distributed work, and the wisdom or folly of incrementalism in design (Dodgson, 2019). The project applied interdisciplinary research in practice to leverage asynchronous situations and distributed networks of designers, strategists, and funders. \n \nThe idea of a large street mural comes to mind; often composed by several artists, it exists in parts executed asynchronously. While some sections are completed to the finest detail, others are simply pencil lines expressing the outline of shapes. While work is asynchronous, so is the advancement of understanding and progress on goals.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".