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Record W2281385639 · doi:10.3390/resources5010011

Environmental Identity and Natural Resources: A Dialogical Learning Process

2016· article· en· W2281385639 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

VenueResources · 2016
Typearticle
Languageen
FieldPsychology
TopicSocial Representations and Identity
Canadian institutionsAthabasca University
Fundersnot available
KeywordsDialogical selfIdentity (music)Context (archaeology)PsychologyNatural (archaeology)EpistemologySociologyMeaning (existential)Social psychologyAestheticsPhilosophyBiology

Abstract

fetched live from OpenAlex

In this article, we elaborate on the role of dialogical learning in identity formation in the context of environmental education. First, we distinguish this kind of learning from conditioning and reproductive learning. We also show that identity learning is not self-evident and we point out the role of emotions. Using Dialogical Self Theory, we then suggest that individuals do not have an “identity hierarchy” but a dialogical self that attaches meaning to experiences in both conscious and unconscious ways. We describe the learning process that enables the dialogical self to develop itself, and we elaborate on the characteristics of a good dialogue. We conclude with some remarks expanding room for a dialogue that would foster identity learning.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score1.000

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)0.0010.000

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 head0.019
GPT teacher head0.323
Teacher spread0.304 · 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