MétaCan
Menu
Back to cohort
Record W3113513946 · doi:10.7577/formakademisk.3384

Leverage analysis

2020· article· en· W3113513946 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFormAkademisk - forskningstidsskrift for design og designdidaktikk · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsOntario College of Art and DesignMemorial University of Newfoundland
FundersMemorial University of Newfoundland
KeywordsLeverage (statistics)Causal loop diagramGenerative grammarComputer scienceStakeholderData scienceManagement scienceSystem dynamicsRisk analysis (engineering)Knowledge managementArtificial intelligenceBusinessEngineeringEconomicsManagement

Abstract

fetched live from OpenAlex

Many systemic design processes include the development and analysis of systems models that represent the issue(s) at hand. In causal loop diagram models, phenomena are graphed as nodes, with connections between them indicating a control relationship. Such models provide mechanisms for stakeholder collaboration, problem finding and generative insight and are powerful . These functions are valued in design thinking, but the potential of these models may yet be unfulfilled. We introduce the notion of “leverage measures” to systemic design, adapting techniques from social network analysis and systems dynamics to uncover key structures, relationships and latent leverage positions of modelled phenomena. We demonstrate their utility in a pilot study. By rethinking the logics of leverage, we make better arguments for change and find the place from which to move the world.

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)0.008
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.869
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.007
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0030.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.343
GPT teacher head0.405
Teacher spread0.062 · 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