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Record W204202411

1 AFFORDANCE AND BEHAVIOR SETTING: A MULTI-LEVEL ECOLOGICAL PERSPECTIVE IN THE STUDY OF THE MEANING OF HABITAT

2014· article· en· W204202411 on OpenAlexaff
Hélène Bélanger, Henny Coolen

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2014
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsAffordanceMeaning (existential)HabitatPerspective (graphical)Congruence (geometry)EcologyFunction (biology)SociologyAction (physics)Social psychologyPsychologyCognitive psychologyComputer scienceArtificial intelligenceBiology
DOInot available

Abstract

fetched live from OpenAlex

Habitat, the environment where people dwell and have their everyday life and activities, has characteristics and features that afford opportunities for social practices and actions, and that communicate meanings. Individuals and collectives, through these social practices and activities, assign meanings to habitat. The relationship between habitat and individuals is thus mutual. But there is no consensus as to how the fit between environments and individuals works. In other words, what is the congruence between people and habitats made of and how can it be studied, and what happens when features of the environment and/or characteristics of people are shaped or changed? This paper proposes a conceptual framework using Barker’s concept of behavior setting and Gibson’s notion of affordance for the study of habitat and its meanings. Habitat can be conceptualized as consisting of several behavior settings (BS). A BS is a higher order environmental structure which is suited to certain behavior patterns. But we question the limits of two important facets of a BS: being a function of collective action alone and being relatively stable in space and time. Environments are used and (re)shaped constantly by participants and other stakeholders. Considering a BS only as a relatively static environmental structure would limit the

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.112
GPT teacher head0.406
Teacher spread0.293 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2014
Admission routes1
Has abstractyes

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