From Unfreezing-Refreezing, to Systems Changes Learning | David Ing | EQ Lab Dialogic Drinks | 2024-03-14/15
Bibliographic record
Abstract
This session is described at https://coevolving.com/commons/2024-03-from-unfreezing-refreezing The field theory (circa 1947) from Kurt Lewin has largely been portrayed as "unfreeze-move-refreeze". In the history of systems thinking, Lewin was foundational for the Socio-Technical Systems (STS) and Socio-Ecological (SES) perspectives of Fred E. Emery and Eric L. Trist. With rising interests in Systems Changes, might we theorize (and philosophize) differently? In 2013, an article on "Rethinking Systems Thinking" was published. In 2019, the Systems Changes Learning Circle was founded in Toronto on an espoused 10-year journey to collectively explore progressing the rich legacy of the systems movement. In 2022-2023, two pilot consulting engagements were conducted, with materials openly accessible under Creative Commons licensing. From 2023, three (academic) peer-reviewed journal articles have been published, and a fourth is in final review. Passing the halfway point in the journey, the Circle now has subgroups on explaining theory and refining practices aligned with a new approach. In this Dialogic Drinks session, we will discuss:. * What if we resequence thinking on "systems" as "genetic-social" before "clockworks"? What if we resequence thinking on "systems changes" as "ecological" before "behavioral"? What if we resequence thinking on "systems changes learning" as "propensity" before "causality"? This DD conversation has been planned with a subsequent deeper philosophical session on "Yinyang and Daojia into Systems Thinking"..
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.137 | 0.126 |
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; both teacher heads agree on what is shown here.
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".