Systems Changes Dialogues on Social Innovation | Centre for Social Innovation Toronto | 2024-03-18
Bibliographic record
Abstract
This session is described at https://coevolving.com/commons/2024-03-systems-changes-dialogues-csi 00:00 Welcome 03:23 Rhythms 05:54 Influences 08:12 Pacing that favours 11:28 Next Meeting (poll) 14:48 Better Questions? Is your social innovation work responding to systems changes… or is it leading to systems changes? Join us for a discussion on how the timing of changes can work for, rather than against, our social impact work. In our next Lunch and Learn, CSI Members David Ing and Kelly Okamura will introduce us to new ways of thinking about organizational systems, paying particular attention to the rhythms, texture, and pacing that make a difference David and Kelly will also address: 1. Which differences make a difference in your social innovation? Which rhythms are normal, and which are shifts? 2. What influences advance or block the rhythmic shifts of your social innovation? 3. Where can the pacing of systems changes, as faster or slower, favour your social innovation? ### About Systems Changes Learning Circle: The Systems Changes Dialogues on Social Innovation are mentored by members of the Systems Changes Learning Circle. The Circle was founded at CSI and OCADU in 2019, on a 10-year journey towards rethinking systems. The mentors are David Ing, Dan Eng, Kelly Okamura and Jenya Nee. There will be a monthly peer-to-peer mutual learning group at CSI, with the first meeting convening in-person at 192 Spadina on Wednesday, March 20 at 4:00pm. A poll on online vs. in-person meetings will determine future scheduling.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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".