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Record W2016592211 · doi:10.1258/jhsrp.2010.009158

Response to ‘The Appropriation of Complexity Theory in Health Care’

2010· letter· en· W2016592211 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Health Services Research & Policy · 2010
Typeletter
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsPricewaterhouseCoopers (Canada)
FundersNational Institute for Health and Care Research
KeywordsFallacyEpistemologyAppropriationRhetorical questionSentenceField (mathematics)RigourArgumentation theoryAdversarySociologyPsychologyPhilosophyLinguisticsComputer scienceMathematics

Abstract

fetched live from OpenAlex

John Paley’s article is good teaching material because it illustrates some classic rhetorical moves used in the ancient sport of argumentation. These include the ‘post hoc ergo propter hoc’ fallacy (assuming that if B occurred after A then A must have caused B); the ‘slippery slope’ fallacy (suggesting that the opponent is guilty of A, then taking the audience through a series of small steps in which A becomes B, B becomes C etc, and concluding that the opponent is guilty of Z); the ‘ad hominem’ fallacy (depicting the opponent as ignorant or foolish, and then concluding that everything s/he says is weak and superficial); and the ‘ad populum’ fallacy (appealing to some positive but ill-defined quality to which the audience is assumed to aspire – such as ‘rigour’ or ‘depth of understanding’). It is ironic, for example, that in the very first sentence of a paper which purports to explain complexity theory, Paley draws a direct and linear link between a series of introductory articles which we published back in 2001 and alleged misunderstandings and misapplications of complexity theory by others in the health care field. Paley claims that in our 2001 series, we ‘partially understood’ what he chose to define as the second principle of complexity – that ‘successive states of the system, globally defined, are determined by previous states, locally defined’. We apparently ‘failed to recognize’ what Paley decided was complexity theory’s first principle – that complexity is an explanatory concept. It is presumably coincidental, then, that Paley chose to use the same example (a termite colony) to illustrate this principle as we ourselves used to illustrate it in the first article in our series. Paley’s third principle of complexity is expressed thus: ‘Complexity explanations account for global order by specifying the local behaviour of units which have no awareness of the order thereby being produced, and which have no intention to produce it’. If, as he claims, we ‘failed to recognize’ this, why did we say in our first article ‘Order, innovation, and progress can emerge naturally from the interactions within a complex system; they do not need to be imposed centrally or from outside. For example, termite colonies construct the highest structures on the planet relative to the size of the builders. Yet there is no chief executive termite, no architect termite, and no blueprint. Each individual termite acts locally, seemingly following only a few simple shared rules of behaviour, within a context of other termites also acting locally. The termite mound emerges from a process of self organization’? Paley suggests that in our article on complexity, leadership and management, we misinterpreted the notion of the self-organizing system to mean that bottom-up approaches to organizational strategy and development should replace top-down ones. Had this been true, it would have been a grievous fault. What Plsek and Wilson actually said was ‘Complexity based organizational thinking suggests that goals and resources are established with a view towards the whole system, rather than artificially allocating them to parts of the system’. The article, written at a time when National Service Frameworks and other rigid, nationally imposed performance targets were stifling local initiative and flexibility, was arguing that tight central control can be counterproductive, not that organizations work better with nobody in charge. The analysis of an argument is incomplete without a consideration of the audience for which it was Trisha Greenhalgh MD, Professor of Primary Health Care, University College London, 206 Holborn Union Building, Highgate Hill, London N19 5LW, UK; Paul Plsek MSc, Independent consultant, Paul E. Plsek & Associates, Inc., Atlanta, USA; Tim Wilson FRCGP, Partner, PricewaterhouseCoopers, London, UK; Sarah Fraser DProf, Director, Sarah Fraser & Associates Ltd, Aylesbury, UK; Tim Holt FRCGP, Clinical Lecturer, University of Warwick, Coventry, UK.

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)0.153
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.218
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1530.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0080.005
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0050.001
Research integrity0.0010.008
Insufficient payload (model declined to judge)0.0000.000

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 head0.289
GPT teacher head0.559
Teacher spread0.270 · 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