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Record W2585331820 · doi:10.1177/0260107916673270

Functional Systems as Metric Forms and Institutions as Non-metric Forms: A Neo-Luhmannian Approach

2017· article· en· W2585331820 on OpenAlexaff
Jean‐Sébastien Guy

Bibliographic record

VenueJournal of Interdisciplinary Economics · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Realism in Sociology
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMetric (unit)SociologyEpistemologyMetric spaceInstitutionalismOpposition (politics)CategorizationSpace (punctuation)Identity (music)Computer scienceMathematical economicsMathematicsPure mathematicsPoliticsPolitical scienceEconomicsPhilosophyLaw

Abstract

fetched live from OpenAlex

The article develops a new pair of fundamental concepts – metric and nonmetric – by exploiting the contrast between systems theory and neo-institutionalism. Borrowed from Manuel DeLanda, the concepts aims at describing the properties of social forms arising in a crowd of individuals functioning as a medium of communication. It is argued that neo-institutionalism privileges nonmetric forms. The crowd is rearranged into distinct groups just like space can be divided into topological zones. Individuals in the crowd take their identity from the group they are in: actor or non-actor, rational or non-rational, etc. By opposition, systems theory emphasizes metric forms as in the case of the functional systems of modern society analyzed by Niklas Luhmann. Each metric form is based on a signal activated by the circulation of individuals inside the crowd and thus creating a modulating flow. Metric forms are not tied to specific individuals. For the flow to go on, it is not necessary for the same individuals who once triggered the signal in the past to return and trigger it again. Anyone will do! Metric forms are truly different from their nonmetric counterparts since they do not categorize individuals by grouping them (or group individuals by categorizing them). JEL: B5, Y8

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.052
GPT teacher head0.378
Teacher spread0.325 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations4
Published2017
Admission routes1
Has abstractyes

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