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Record W4376603477 · doi:10.1177/00218863231174957

Social Fields: Knowing the Water We Swim in

2023· article· en· W4376603477 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.

Bibliographic record

VenueThe Journal of Applied Behavioral Science · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsConcordia University
Fundersnot available
KeywordsAffordanceField (mathematics)Leverage (statistics)SociologySocial changeEpistemologyIntervention (counseling)Perspective (graphical)AutonomyPsychologyComputer sciencePolitical scienceCognitive psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

While the term ‘social field’ has surfaced sporadically in various disciplines throughout the twentieth Century, it has largely lain dormant as a conceptual framework. In this article, we re-introduce the social field as a foundational concept both for understanding collective lived experience and for developing methodologies to effect systems change. We explore and expand on three inter-related properties that we consider to be phenomena common to all social fields: intercorporeality, autonomy, and affordance. Drawing on recent and emerging intervention methodologies focusing on these properties, we illustrate the potential of taking a social field perspective for both diagnosis and intervention in the change process. We make the case that the social field is a distinct entity and a powerful leverage point for effecting systems change, and that the re-invigoration of social field theory and practice can make a significant contribution to the field of organizational and systems change.

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)0.021
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0030.001
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 head0.281
GPT teacher head0.462
Teacher spread0.182 · 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