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Record W335101932 · doi:10.1596/9610

The Capacity to Evaluate : Why Countries Need It

2006· article· en· W335101932 on OpenAlexaboutno aff
Linda Morra-Imas, Ray C. Rist

Bibliographic record

VenueWorld Bank, Washington, DC eBooks · 2006
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsMandateCapacity buildingGeneral partnershipTransformative learningCapacity developmentPoverty reductionProcess managementProcess (computing)Corporate governanceInternational developmentBusinessDeveloping countryWork (physics)PovertyComputer sciencePolitical scienceEconomic growthEngineeringEnvironmental economicsEconomicsPsychologyPedagogyFinance

Abstract

fetched live from OpenAlex

Evaluation skills are central to
\n effective development work. Evaluation captures real
\n results, leads to feedback and learning, and identifies
\n areas where more capacity is needed. It is also an essential
\n tool for making mid-course corrections in ongoing programs,
\n developing appropriate indicators, tracking an
\n individual's or organization's capacity to deliver
\n on its mandate, and guiding the design of future
\n programming. Donors now expect countries to be full partners
\n in the development process, which means that they need to
\n have the capacity to evaluate their own progress and to use
\n the findings to continuously improve their performance. The
\n evidence suggests that these changes can potentially have a
\n transformative effect on governance and make poverty
\n reduction efforts dramatically more effective. The World
\n Bank, in partnership with Carleton University in Ottawa, is
\n currently providing evaluation capacity development through
\n its International Program for Development Evaluation
\n Training (IPDET), which has already trained more than 850
\n practitioners from 100 countries.

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)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.105
GPT teacher head0.396
Teacher spread0.291 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2006
Admission routes1
Has abstractyes

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