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Record W2960695017 · doi:10.29173/jjs51s

Generational Attention: Remembering How to Be a People

2012· article· en· W2960695017 on OpenAlexvenueno aff
Peter T. Dunlap

Bibliographic record

VenueJournal of Jungian Scholarly Studies · 2012
Typearticle
Languageen
FieldPsychology
TopicAcademic and Historical Perspectives in Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsHumanityPoliticsNaturalismFeelingPsychologyAnalytical psychologyFunction (biology)SociologyEnvironmental ethicsAestheticsSocial psychologyPsychoanalysisEpistemologyPolitical scienceArtLawPhilosophy

Abstract

fetched live from OpenAlex

In the aftermath of World War I C. G. Jung responded to the destruction wrought by humankind by imagining humanity as a single, semi-conscious being. Jung imagines a naturalistic God capable of helping us remember how to be a people by using an awareness of all of human history to guide the species’ decision-making. As a psychologist I use Jung’s image to cultivate this “generational attention” in progressive political groups, particularly helping them cultivate the “public emotional intelligence” necessary to bind themselves to one another as a human community. Today the Jungian community can contribute to the articulation of a uniquely Jungian political psychology by following this image and by integrating a more differentiated feeling function in our organizations as we go.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.097
GPT teacher head0.387
Teacher spread0.291 · 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 designNot applicable
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

Citations1
Published2012
Admission routes1
Has abstractyes

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