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Record W4297819726 · doi:10.1111/jtsb.12360

A Motivational Theory of Roles, Rewards, and Institutions

2022· article· en· W4297819726 on OpenAlexaff
Seth Abrutyn, Omar Ližardo

Bibliographic record

VenueJournal for the Theory of Social Behaviour · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAction (physics)NormativeArgument (complex analysis)PsychologyCognitive scienceEpistemologyKey (lock)CognitionNothingCognitive psychologySociologySocial psychologyComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

Abstract In this paper, we attempt to rehabilitate the notion of role by linking sociological role theory to recent work on motivational, affective, and cognitive neuroscience specifying the internal mechanisms behind motivated action. We argue that there is nothing inherently problematic or retrogressive in the idea of “role,” once its link to a purely normative account of motivated action is severed. Instead, by conceptualizing roles as emerging and persisting around structured reward systems, we are able to incorporate contemporary motivational science such that rewards become the proximate causal mechanisms currently missing in role theory. Consequently, a key implication of our argument is that the best way to link role, action, and structures is by reviving the idea of institutions as literal reward systems, which allows us to envision roles as the mechanisms via which the pursuit and delivery of rewards and goal‐objects are routinized. Implications for a motivational theory of roles, rewards, and institutions is discussed.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.005
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.011
Scholarly communication0.0040.006
Open science0.0010.002
Research integrity0.0010.002
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 head0.034
GPT teacher head0.261
Teacher spread0.227 · 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

Citations21
Published2022
Admission routes1
Has abstractyes

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