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Record W7135165162

Introducing Military Art to Interventions in Systems Oriented Design

2023· article· en· W7135165162 on OpenAlexaff
Thomas Slensvik

Bibliographic record

VenueOCAD University Open Research Repository (OCAD University) · 2023
Typearticle
Languageen
FieldEngineering
TopicMilitary Strategy and Technology
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsPsychological interventionMindsetContext (archaeology)AdaptabilityAction (physics)Portfolio
DOInot available

Abstract

fetched live from OpenAlex

This paper discusses the integration of traditional military concepts into systems oriented design (SOD) to enhance interventions in societal systems. It introduces three central concepts in military operational art: friction, culmination, and synchronisation. The concept of friction, as defined by Clausewitz, refers to the challenges, uncertainties, and obstacles that arise during the execution of military operations. When applied to SOD, it emphasises the need for adaptability and adjusting interventions based on a deeper understanding of the system being intervened in. Culmination, another concept from Clausewitz, highlights the critical moment when a successful action begins to decline. In the context of societal interventions, it becomes important to assess the limits of an intervention, recognising when it no longer remains productive. Synchronisation, a concept that emerged with the complexity of military operations, involves coordinating and harmonising diverse resources, capabilities, and actions to overcome resistance and achieve desired outcomes. In design and system interventions, a similar concept called Portfolio Interventions exists, which emphasises the need for coordinated actions and the linking of interventions within a portfolio. The abstract concludes by emphasising the importance of experimenting whit other concepts from different praxeologies into SOD while maintaining a designerly mindset. It recognises the challenges of balancing mindset and methods and highlights the need to further refine and develop the beneficial aspects of these methods. Ultimately, the goal is to advance SOD and make interventions more successful in addressing complex societal issues.

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)0.019
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.019
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.029
Scholarly communication0.0100.009
Open science0.0020.008
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0080.001

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 head0.074
GPT teacher head0.287
Teacher spread0.214 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations0
Published2023
Admission routes1
Has abstractyes

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