Bibliographic record
Abstract
This paper explores the capacity of complex systems to find their own form of order and coherence, often referred to in terms of self-organisation emergent change, then asking the question, 'What can organisational leaders do to create the systems and structures that would facilitate emergent change?' Emergent change comes from within and through the active members of a system and is not imposed according to some external prompting or design. This results in the sort of change capacity that enables an organisation to be agile and resilient in highly volatile times. I have identified seven key organisation-specific factors that facilitate emergent change. These include: executive engagement, purposeful orientation, a culture of experimentation, a safe-fail culture, collaborative decision-making, collaborative quality measures, and intentional learning. These factors were initially identified through an extensive literature review, interviews with the CEOs of 15 Canadian healthcare organisations, and a think tank of subject matter experts. This resulted in the use of exploratory factor analysis to validate a survey that can be used to assess the presence or absence of these factors in a specific organisation, thus providing leaders with a framework for change.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".