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Record W2917601515 · doi:10.1017/9781316823279.017

Reconceptualizing Intrinsic Motivation

2019· book-chapter· en· W2917601515 on OpenAlexaff
Barry Schwartz, Amy Wrzesniewski

Bibliographic record

VenueCambridge University Press eBooks · 2019
Typebook-chapter
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPleasureRelation (database)PsychologyEpistemologyField (mathematics)Intrinsic motivationPoint (geometry)Social psychologyCognitive psychologySociologyPhilosophyComputer scienceMathematics

Abstract

fetched live from OpenAlex

There is a long history of thought and research in the social sciences that views human beings as engaged in entirely instrumental activities in pursuit of goals that typically give them pleasure. This view makes a sharp distinction between "means" and "ends," and treats the relation between means and ends as essentially arbitrary. Forty years of research on "intrinsic motivation" presents a different view, suggesting that some activities are themselves ends. In this chapter, we argue that distinguishing between intrinsic and extrinsic motivation has been important, but that the current understanding of the distinction is not adequate to capture the most important dimensions of difference between these two types of motives. We suggest a modification of the distinction, between activities that are pursued for consequences that bear an intimate relation to the activities themselves, and those that are purely instrumental. We call the former class of activities "internally motivated," and argue that while they are not necessarily pleasurable, they yield lasting effects on well-being that instrumental consequences typically do not. Further, we argue that internally motivated activities differ from intrinsically motivated ones, in which the sheer pleasure of the activity motivates its pursuit. We discuss evidence from both laboratory research and field studies, including a longitudinal study of West Point cadets, in support of our arguments.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.880
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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.094
GPT teacher head0.296
Teacher spread0.202 · 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.

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

Citations28
Published2019
Admission routes1
Has abstractyes

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