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Record W3010415872 · doi:10.1097/naq.0000000000000406

Hard Science and “Soft” Skills

2020· article· en· W3010415872 on OpenAlexaff
Sandra Davidson

Bibliographic record

VenueNursing Administration Quarterly · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMetaphorPerspective (graphical)Bridge (graph theory)SociologyEpistemologyComplexity scienceValue (mathematics)Health careEngineering ethicsKnowledge managementPsychologyManagement scienceComputer sciencePolitical scienceMedicineArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Since the 1990s, complexity science has been utilized as a metaphor for understanding health care organizations and new ways of leading within them. In this article, 3 principles of complexity leadership put forth by Porter-O'Grady and Malloch in the text Quantum Leadership are explored: (1) wholes are not just the sum of their parts; (2) all health care is local; and (3) value is now the centerpiece of service delivery. Each of these principles is discussed from a 20th-century "organization as machine" perspective, a complexity science perspective, and a complex relational processes (CRP) view. The CRP lens provides a useful bridge from the hard science (nonhuman) systems metaphor to what we often think of as the soft skills of relationship building and communication. CRP does this by drawing on philosophy and the social sciences of sociology and psychology as a way to humanize the nonhuman metaphors of complexity science. This opens up new ways of understanding and talking about leadership in organizations. This shifts our traditional thinking of individuals as leaders to a more relational process of complex relational leading that occurs between people within organizations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0030.046
Scholarly communication0.0080.008
Open science0.0010.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.001

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 head0.129
GPT teacher head0.409
Teacher spread0.280 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations9
Published2020
Admission routes1
Has abstractyes

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