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Record W2794444965 · doi:10.1111/gove.12342

The twin faces of public sector design

2018· article· en· W2794444965 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueGovernance · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsUniversity of TorontoCarleton University
FundersEmployment and Social Development Canada
KeywordsInstrumentalismLeverage (statistics)PoliticsCorporate governanceSociologyPublic policyTop-down and bottom-up designDesign thinkingEconomicsPublic relationsPositive economicsManagement sciencePolitical scienceEpistemologyManagementComputer scienceEconomic growthLaw

Abstract

fetched live from OpenAlex

Design thinking has become a popular approach for governments around the world seeking to address complex governance challenges. It offers novel techniques and speaks to broader questions of who governs, how they govern, and the limits of rational instrumentalism in policy making. Juxtaposing design thinking with an older tradition of policy design, this article offers the first critical analysis of the application of design thinking to policy making. It argues that design thinking does not sufficiently account for the political and organizational contexts of policy work. Design thinking also errs in universally privileging one particular policy style over others, and fails to account for the reality of policy mixes. Despite these deficiencies, it is argued that design thinking can inform and enrich governance by helping policy designers produce more adaptable designs, better appreciate the behavioral dynamics of public sector design, and leverage networked approaches to social problem solving.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score0.228

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.057
GPT teacher head0.232
Teacher spread0.175 · 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