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IEG's Role in Evaluating Climate Financing—Response

2012· article· en· W2327372734 on OpenAlexaff
Simon D. Donner, Milind Kandlikar, Hisham Zerriffi

Bibliographic record

VenueScience · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCredibilityAuditInstitutionBusinessFinancial institutionAccountingIndependence (probability theory)Corporate governanceFinancePolitical science

Abstract

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Heider asserts the importance of avoiding bias or conflict of interest in evaluating the impacts of climate change financing. We could not agree more. Independent and transparent auditing of the Green Climate Fund (GCF) and other climate change financing is not only critical to minimizing waste, but also to building the public and political will necessary to provide financial support to the developing world. We recognize that internal auditing bodies such as the Independent Evaluation Group (IEG) of the World Bank Group try to maintain independent governance structures and implement institutional mechanisms aimed at minimizing bias in project evaluation. Unfortunately, there is substantial evidence that historical and on going ties between an auditor and the aid institution create the potential for both actual and perceived bias in project evaluation. Maintaining independence and credibility is a challenge for independent evaluation offices because of shared culture and personnel. There is a revolving door between international development institutions and their internal evaluation groups ([ 1 ][1]–[ 3 ][2]). For example, a majority of the current upper management (directors, managers, program leaders, and advisers) at the IEG are former World Bank employees, in some cases for decades. The IEG itself is housed within the World Bank headquarters. It is unlikely that evaluators with long-term ties to the aid institution can conduct investigations free from concern about potential repercussions on a future career in the institution ([ 1 ][1]). Even if the evaluators are independent, the culture of the institution still affects their outlook and their methods. An external review of the Internal Evaluation Office of the International Monetary Fund (IMF) found that evaluators were often unable to think outside the box due to the influence of IMF culture and recommended that outsiders be recruited to bring fresh personalities, perspectives, and questioning attitudes ([ 1 ][1]). It is for these cultural reasons that there have been calls for evaluations of aid institutions to be conducted by people without ties to the institutions ([ 3 ][2]–[ 5 ][3]). In the case of climate change financing, the perception of the trustees and the auditing process could influence whether donor nations meet funding pledges and whether recipient nations trust financing programs. Regardless of recent initiatives to increase aid effectiveness and introduce a culture of learning to aid institutions, the perception of a conflict of interest between the auditor and the aid institution would remain. As Heider notes, this problem would not be solved by delegating evaluation to a single outside entity that could become financially dependent on the institutions it was meant to monitor. These actual and perceived conflicts of interest can be minimized by engaging a loose, third-party network of auditors through an academic-style peer review system. The internal evaluation divisions at the development banks and aid agencies would still be key players in such a system. For example, if the World Bank becomes the GCF trustee, the IEG could play a more editorial role that includes collecting project data, coordinating the external evaluation process, and reporting results of that process to the GCF Board. This approach would take advantage of the strengths of the IEG while providing the type of transparent auditing necessary to build the political and public confidence in the climate change financing system. 1. [↵][4] 1. K. Lissakers, 2. I. Husain, 3. N. Woods , Report of the External Evaluation of the Independent Evaluation Office (International Monetary Fund, Washington, DC, 2006). 2. 1. C. Weaver , Rev. Int. Org. 5, 365 (2010). [OpenUrl][5][CrossRef][6] 3. [↵][7] 1. A. Lerrick , “Is the World Bank's word good enough?,” Testimony before the U.S. Senate Foreign Relations Committee, Hearing on Multilateral Development Banks (U.S. Government Printing Office, Washington, DC, 2006). 4. 1. R. Levine , “Evaluating development aid effectiveness,” Testimony before the U.S. Senate Foreign Relations Committee, Hearing on Multilateral Development Banks (U.S. Government Printing Office, Washington, DC, 2006). 5. [↵][8] 1. W. Easterly , “Accountability for multilateral development banks,” Testimony before the U.S. Senate Foreign Relations Committee, Hearing on Multilateral Development Banks (U.S. Government Printing Office, Washington, DC, 2006). [1]: #ref-1 [2]: #ref-3 [3]: #ref-5 [4]: #xref-ref-1-1 View reference 1 in text [5]: {openurl}?query=rft.jtitle%253DRev.%2BInt.%2BOrg.%26rft.volume%253D5%26rft.spage%253D365%26rft_id%253Dinfo%253Adoi%252F10.1007%252Fs11558-010-9094-1%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [6]: /lookup/external-ref?access_num=10.1007/s11558-010-9094-1&link_type=DOI [7]: #xref-ref-3-1 View reference 3 in text [8]: #xref-ref-5-1 View reference 5 in text

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)0.051
metaresearch head score (Gemma)0.119
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.051
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.119
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0050.012
Scholarly communication0.0160.010
Open science0.0050.021
Research integrity0.0220.022
Insufficient payload (model declined to judge)0.0110.006

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 head0.277
GPT teacher head0.575
Teacher spread0.299 · 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
GenreCommentary

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".

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Citations31
Published2012
Admission routes1
Has abstractyes

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