Dialogue: Friends or foes? Theory of change, systemic design (thinking), and systems change(s) learning
Bibliographic record
Abstract
In this dialogue, we will facilitate a discussion amongst RSD10 participants on the compatibilities and incompatibilities between (i) Theory of Change (ToC), (ii) Systemic Design (Thinking) (SDT) and (ii) Systems Change(s) Learning (SCL). \nAs a trigger question, we will start with: Do ToC, SDT and SCL overlap to a greater or less extent? Can or should that overlap see further integration or separation? \nThe three approaches have been discussed as separate topics in prior RSD meetings. At RSD9, Peter Jones reviewed current uses of Theory of Change in practice, in negotiations between (philanthropic) funders and changemakers. Oriented towards linear logic models, simplistic presentations may not well represent the complexities of aspirations for change. Systemic design has been at the core of RSD meetings, originating from the popularization of design thinking (e.g. from IDEO) (Brown, 2008), into the body of work now shepherded by the Systemic Design Association. The Systemic Design Toolkit (Van Ael et al., 2018) has emphasized practical frameworks and designerly methods (Jones, 2018). The systems turn with design enlarges the vision from the heritage in products, through services, into complex social systems (Jones, 2017). Systems changes learning has been introduced in two previous RSD meetings, coming from the systems sciences community (Khan, 2019, Khan, Ing, et al., 2020). The popularization of organisations seeking “systems change” may be systemic or systematic in nature. Foundations may be espoused in systems thinking, cybernetics or complexity science, yet that appreciation for ways in which wicked problems in the present are transitioned into better outcomes in the future is not always clear. The systems changes learning approach appreciates natures that may require reframing beyond anthropocentric presuppositions. We welcome a dialogue to explore the variety of perspectives and understandings on ways in which a synthesis of ToC, SDT and SCL is possible and/or desirable. \nFormat \nModeration: Zaid Khan and David Ing Agenda: Introduce concepts Suggested questions for dialogue Group dialogue Summary Facilitation: members of Systems Changes Learning Circle This workshop will be led by members of the Systems Changes (SC) Learning Circle – based out of Toronto, Canada. Started in 2019, the Circle is on a 10-year journey to develop methods based on multiparadigm inquiry that integrates a variety of schools of thought. On our journey, the Circle has previously shared its progress at RSD8 (Khan & Ing, 2019) and RSD9 (Ing, Khan et al., 2020).
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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 | 0.092 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.088 |
| Scholarly communication | 0.032 | 0.051 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.016 | 0.026 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".