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Record W2954634883 · doi:10.29173/jjs36s

How Do We Transform our Large-Group Identities?

2017· article· en· W2954634883 on OpenAlexvenueno aff
Peter T. Dunlap

Bibliographic record

VenueJournal of Jungian Scholarly Studies · 2017
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPrejudice (legal term)ConscienceGroup (periodic table)Function (biology)Social psychologyEpistemologyPsychologySociologyFocus groupSocial groupPhilosophyAnthropology

Abstract

fetched live from OpenAlex

While Jung was particularly critical of groups, he also had a vision of humans as a selfaware, problem-solving being. In this paper I explore his interest in and critique of, or prejudice against, groups. I raise the issue of whether it is possible to transform consciously our large-group identities, thus making them more morally responsive to the conditions they find themselves in. I focus specifically on recent social science research regarding the psychocultural function of emotion and group theory and practice that could be used by a new generation of “psychocultural practitioners” to develop the theory and practices of large-group transformation that would aid in the moral development of a group’s conscience. I also propose that the Jungian communities could participate in the founding of such a distinctive vocation, which would formalize numerous informal and innovative practices that many are already engaged in.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.045
Scholarly communication0.0110.022
Open science0.0010.007
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0060.002

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 head0.069
GPT teacher head0.404
Teacher spread0.335 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations2
Published2017
Admission routes1
Has abstractyes

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