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Record W4414350851 · doi:10.1139/facets-2025-0011

Blossoming instantiations in FRAM: a temporal tensor framework for socio-technical systems

2025· article· en· W4414350851 on OpenAlex
Andrea Falegnami, Andrea Tomassi, Elpidio Romano

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFACETS · 2025
Typearticle
Languageen
FieldMathematics
TopicTensor decomposition and applications
Canadian institutionsnot available
Fundersnot available
KeywordsSociotechnical systemAmbiguityEncoding (memory)Dimension (graph theory)Scheme (mathematics)

Abstract

fetched live from OpenAlex

This article presents a novel encoding scheme for the Functional Resonance Analysis Method (FRAM) to address its ambiguity about the time concept in sociotechnical systems analysis. The scheme introduced is a tensor-based encoding that allows for the dynamic temporal dimension to be natively incorporated into the FRAM model, thereby overcoming the method’s traditional limitation of static representation. By integrating tensors for single instantiations and evolutionary pathways of sociotechnical systems—namely, emergent pathways, the framework enhances the descriptive power of FRAM, enabling a deeper understanding of system behaviour over time. The proposed approach reframes the entire FRAM as a tool depicting sociotechnical systems by enriching the description of emergent and calculated instantiations, and suggests potential applications based on this underlying encoding scheme, thereby expanding the method’s applicability in understanding and managing complex sociotechnical systems.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.598
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.057
GPT teacher head0.385
Teacher spread0.329 · 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