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Record W2316547733 · doi:10.3138/cjpe.016.002

The Strong Focus on Output Information: A Threat to Evaluation in the Swedish State Sector?

2001· article· en· W2316547733 on OpenAlexvenueno aff
K.H. Haag, Fredrik Rosengren

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2001
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsPublic sectorPublic interestState (computer science)Quality (philosophy)Focus (optics)BusinessInformation managementPrivate sectorManagement information systemsInformation systemPublic relationsEconomicsPublic economicsPolitical scienceEconomic growthComputer scienceManagement

Abstract

fetched live from OpenAlex

Abstract: This article discusses the issue of combining different kinds of results information for decision-making in the public sector. Its purpose is to take part in the debate on public management and management by results, by arguing that the trend in public management is a focus on information about outputs and a decreasing interest in information about outcomes. Since the mid-1980s there has been an obvious drop in the interest in results information in the public sector in Sweden and in many other countries (those within the OECD for instance). The favoured concept for the state sector in Sweden is management by results. Ministries and agencies have developed skills to measure not only costs, but also quality and performance. It is easy to believe that this development also should stimulate the development of evaluations of outcomes and in-depth analyses, as these evaluations are important to the concept of management by results. However, we argue that simpler and more focused follow-up information has increased and so also has interest in such information. In short, interest in information about outputs has increased at the expense of interest in information about outcomes. This threatens to diminish the future role of evaluation.

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)0.045
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.954
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0450.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.392
GPT teacher head0.506
Teacher spread0.114 · 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 designOther design
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

Citations1
Published2001
Admission routes1
Has abstractyes

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