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Varieties of the Self From Self-Esteem to Self-Control

2020· reference-entry· en· W3014318825 on OpenAlexaff
Michael Pettit

Bibliographic record

VenueOxford Research Encyclopedia of Psychology · 2020
Typereference-entry
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsYork University
Fundersnot available
KeywordsPoliticsSelf-esteemSelfSelf-governanceState (computer science)Welfare stateSociologyPolitical sciencePolitical economyPsychologySocial psychologyLawCorporate governanceEconomics

Abstract

fetched live from OpenAlex

Abstract Various self-concepts constitute major keywords in both psychological science and liberal political discourse. They have been central to psychology’s public-facing, policy-oriented role in the United States, dating back to the mid-19th century. Psychologists’ articulations of self-concept include an understanding of the individual, society, and the interventions needed to augment them both. Psychologists’ early enthusiasm for self-esteem has given way to competing concepts of the individual, namely self-regulation and self-control. Self-esteem in a modern sense coalesced out of the deprivation of the Great Depression and the political crises it provoked. The fate of self-esteem became tied to the capacities of the liberal welfare state to improve the psychic capacities of its citizens, in order to render them both more equal under the law and more productive in their daily existence. Western democracies, especially the United States, hit peak self-esteem in early 1990s. Since then, psychologists lost faith in the capacity of giving away self-worth to improve society. Instead, psychologists in the 21st century preached a neo-Victorian gospel of self-reliance. At the very historical juncture when social mobility became more difficult, when inherited social inequality became more entrenched, psychologists abandoned their Keynesian model of human capital and embraced its neoliberal counterpart.

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)0.002
metaresearch head score (Gemma)0.002
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: Other · Consensus signal: none
Teacher disagreement score0.745
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.001
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 head0.092
GPT teacher head0.427
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2020
Admission routes1
Has abstractyes

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