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Record W141110725

Facilitating emergence: Complex, adaptive systems theory and the shape of change

2012· article· en· W141110725 on OpenAlex
Peter Dickens

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOhioLink ETD Center (Ohio Library and Information Network) · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsnot available
Fundersnot available
KeywordsComplex adaptive systemComputer scienceArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

This study used Principal Component Analysis to examine factors that facilitate emergent change in an organization.As organizational life becomes more complex, today's dominant management paradigms no longer suffice.This is particularly true in a health care setting where multiple sources of disease interacting with each other meet with often-competing organizational priorities and accountabilities in a highly complex world.This study identifies new ways of approaching complexity by embracing the capacity of complex systems to find their own form of order and coherence.Based on a review of the literature, interviews with hospital CEOs, and my organization development practice experience in the health care sector, I identified nine constructs of interest: a strategic framework; organizational culture; work structures; CEO and executive team; leadership culture; quality control systems; accountability framework; learning structures; and feedback processes.One hundred and sixty-two senior leaders, managers, and staff at a hospital in Toronto, Canada, who had completed an eight-week leadership program, completed an Emergence Survey based on the nine constructs of interest.The survey included Likert items representing the nine constructs, as well as opportunities to provide narrative feedback.In the initial analysis of the survey results, the items taken as a whole would not converge on a clear set of components.It was also clear that the mean for most of the items was very high.I theorized that the size of the sample and possibility that they were a favorably biased convenience sample because they had self-selected as leaders may have contributed to the lack of convergence and high mean.I then theorized three clusters of constructs, based on what appeared to be natural affinities.At that point I facilitated two focus groups with people who were among the survey group.Both focus groups affirmed the importance of each of the factors in improving organizational performance indicators such as patient satisfaction, staff v engagement, and quality.I then completed a principal component analysis of each of the three clusters of constructs.From this analysis, seven components emerged.Five of these, executive engagement, safe-fail culture, collaborative decision-processes, a collaborative quality, and intentional learning processes had reliability >.70; culture of experimentation and purposeful orientation had reliability < .70.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.010
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.107
GPT teacher head0.307
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it