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The Life Span Development of Generativity

2020· reference-entry· en· W3005190021 on OpenAlexaff
Michael W. Pratt, M. Kyle Matsuba, Heather L. Lawford, Feliciano Villar

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsBishop's UniversityKwantlen Polytechnic UniversityWilfrid Laurier University
Fundersnot available
KeywordsGenerativityErikson's stages of psychosocial developmentPersonalityPsychologyPersonality developmentNarrativeNarrative identityLife spanContext (archaeology)Adult developmentDevelopmental psychologyConstruct (python library)Identity (music)Meaning (existential)FlourishingSocial psychologyGerontologyAesthetics

Abstract

fetched live from OpenAlex

This chapter addresses the development of generativity, Erikson’s conception of the midlife strength in his eight-stage model of personality development. Following Erikson, the authors define generativity as care for next generations and set it in the context of both personality theory and life span development. Specifically, the authors draw on the framework of McAdams that characterizes personality as composed of three sequentially developing levels: actions, goals/motives, and the narrative life story (a mature form of narrative that provides the self with a sense of meaning and identity). The authors then review research on generativity as expressed from adolescence to later adulthood, which indicates that it is a relevant construct across this entire period in a variety of life domains. They also consider factors influencing generativity levels, including family background and cultural variations. Throughout the chapter, the authors consider the connections of generativity to morality across different facets of personality and stages of the adult life span.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.079
GPT teacher head0.335
Teacher spread0.257 · 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 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

Citations8
Published2020
Admission routes1
Has abstractyes

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