Open Sourcing Social Change: Inside the Constellation Model
Bibliographic record
Abstract
The constellation model was developed by and for the Canadian Partnership for Children's Health and the Environment. The model offers an innovative approach to organizing collaborative efforts in the social mission sector and shares various elements of the open source model. It emphasizes self-organizing and concrete action within a network of partner organizations working on a common issue. Constellations are self-organizing action teams that operate within the broader strategic vision of a partnership. These constellations are outwardly focused, placing their attention on creating value for those in the external environment rather than on the partnership itself. While serious effort is invested into core partnership governance and management, most of the energy is devoted to the decision making, resources and collaborative effort required to create social value. The constellations drive and define the partnership. The constellation model emerged from a deep understanding of the power of networks and peer production. Leadership rotates fluidly amongst partners, with each partner having the freedom to head up a constellation and to participate in constellations that carry out activities that are of more peripheral interest. The Internet provided the platform, the partner network enabled the expertise to align itself, and the goal of reducing chemical exposure in children kept the energy flowing. Building on seven years of experience, this article provides an overview of the constellation model, discusses the results from the CPCHE, and identifies similarities and differences between the constellation and open source models.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".