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Record W4224066054 · doi:10.30636/jbpa.51.292

What is Behavioral in Policy Studies?

2022· article· en· W4224066054 on OpenAlexaff
Michael Howlett, Ching Leong

Bibliographic record

VenueJournal of Behavioral Public Administration · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsIncentiveUtilitarianismPositive economicsEconomicsRationalityAction (physics)CurrencyBehavioral economicsSubsidyPublic economicsPolitical scienceMicroeconomicsMacroeconomicsLaw

Abstract

fetched live from OpenAlex

The recent behavioral turn among economic, administrative and other scholars has resulted in a new way of thinking about policy sciences which emphasizes behavioural insights and the need for greater research into this facet of policy-making. While most early researchers had aspired to the hallmarks of social science, with theoretical modelling on the assumption of microeconomic utility, many now have come to accept that this kind of rationality may be in short supply in practice and that more study of norms, irrationalities and collective action is required. This new focus has led to a behavioural turn in policy theory and practice. Policy design, in particular, now addresses a much wider range of policy tools and is no longer as circumscribed by a priori adherence to utilitarian assumptions about policy behavior as it was in the past. At the same time however, this turn presents new challenges including the irreducibility of incentives for behavior to a single utilitarian currency. We argue that the policy sciences still need a more serious consideration of non-economic incentives, if they are to move away from the traditional utilitarianism which has coloured findings in the discipline for decades.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
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 head0.139
GPT teacher head0.464
Teacher spread0.324 · 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 designQualitative
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

Citations9
Published2022
Admission routes1
Has abstractyes

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