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Record W1608571636 · doi:10.1300/j130v04n03_06

Capacity Building for Environmental Management in Indonesia

2000· article· en· W1608571636 on OpenAlexafffundabout
James H. Bater, Len Gertler, Haryadi Haryadi, David Knight, S. Martopo, B. Mitchell, Geoffrey Wall

Bibliographic record

VenueJournal of Transnational Management Development · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsUniversity of Waterloo
FundersUniversitas IndonesiaUniversity of WaterlooInternational Development Research Centre
KeywordsCapacity buildingAgency (philosophy)Sustainable developmentBusinessEnvironmental resource managementEnvironmental planningValue (mathematics)Environmental economicsProcess managementEconomic growthComputer sciencePolitical scienceEconomicsGeographySociology

Abstract

fetched live from OpenAlex

There is considerable value in learning from experience in capacity building projects, and this paper provides a self-evaluation regarding the interactive learning approach applied in the Bali Sustainable Development Project (BSDP). The BSDP was part of a larger institutional capacity building and human resource development project supported by the Canadian International Development Agency. Experiences related to contextual and substantive aspects are reviewed. Particular attention is given to the need for incorporating culture into sustainable development strategies, developing an iterative and adaptive approach, and capitalizing on synergistic opportunities.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0040.002
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.019
GPT teacher head0.264
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2000
Admission routes3
Has abstractyes

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