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Record W2792013179 · doi:10.1177/1035719x1601600103

Performance measurement as precursor to organizational evaluation capacity building

2016· article· en· W2792013179 on OpenAlexaff
Isabelle Bourgeois

Bibliographic record

VenueEvaluation Journal of Australasia · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsNational Circus School
Fundersnot available
KeywordsTransparency (behavior)Performance measurementBusinessCapacity buildingProcess managementOrganizational performanceKnowledge managementBridge (graph theory)Computer scienceRisk analysis (engineering)Management scienceMarketingEngineeringEconomicsComputer securityMedicine

Abstract

fetched live from OpenAlex

The widespread use of performance measurement and program evaluation in public administrations worldwide has been met with varying degrees of success, in terms of increased transparency and effectiveness. In many cases, the lack of impact of these functions is attributed to insufficient organizational knowledge and capacity to implement proper monitoring and evaluation systems. Both of these management tools are recognized in their own right as having the potential to contribute to ongoing decision-making and budgetary allocations within public organizations; however, they have complementary roles in terms of producing ongoing versus periodic information, and focusing on outputs and early outcomes versus longer-term program objectives. This paper attempts to bridge these two functions by proposing that performance measurement can act as a precursor to the development of organizational evaluation capacity, by providing some of the building blocks required to develop an evaluative culture within an organization. Five models of organizational evaluation capacity were analyzed to identify the extent to which performance measurement contributes to evaluation capacity building.

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)0.019
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.242
GPT teacher head0.434
Teacher spread0.192 · 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 designObservational
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

Citations3
Published2016
Admission routes1
Has abstractyes

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