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Record W7063511757

Adaptive Management in SDC: Challenges and Opportunities

2020· other· en· W7063511757 on OpenAlexfundno aff

Bibliographic record

VenueOpenDocs (Institute of Development Studies) · 2020
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersGlobal Affairs CanadaUnited States Agency for International DevelopmentDepartment of Foreign Affairs and Trade, Australian GovernmentInternational Development Research CentreStyrelsen för Internationellt UtvecklingssamarbeteDepartment for International Development
KeywordsFlexibility (engineering)Adaptive managementProcess (computing)Context (archaeology)Adaptation (eye)Complexity managementBest practiceStakeholderOrder (exchange)Corporate governance
DOInot available

Abstract

fetched live from OpenAlex

Adaptive management (AM) is a programme management approach that helps international development organisations to become more learning-oriented and more effective in addressing complex development challenges. AM practices have been applied for decades within other sectors as varied as logistics, manufacturing, product design, military strategy, software development and lean enterprise. At its core, AM is not much more than common sense, as it essentially recognises that the solutions to complex and dynamic problems cannot be identified at the outset of a programme but need to emerge throughout the process of implementation as a result of systematic and intentional monitoring and learning. The generic AM process typically involves an iterative cycle of design, implementation, reflection and adaptation activities, supported both by system monitoring and stakeholder involvement to obtain a better understanding of the evolving system and improve how the intervention is managed. \n \nA favourable context for AM in development. During recent decades, the international development sector has aimed to increase its results and impact orientation. As a result, a growing number of development organisations and governments have become increasingly aware of the limitations of traditional ‘linear and prescriptive’ programming approaches. They are now recognising the need to handle complexity better, and have begun to adapt their policies and practices to facilitate adaptive approaches. The World Bank, for example, now acknowledges that aid agencies need to increase flexibility of implementation, tolerate greater risk and ambiguity, devolve power from aid providers to aid partners, and avoid simplistic linear schemes for measuring results. Multilateral and bilateral organisations such as the World Bank, the United Kingdom’s (UK) Department for International Development (DFID) and the United States Agency for International Development (USAID) are currently experimenting with adaptive approaches. A multitude of adaptive approaches and communities of practice have emerged that aim to improve the effectiveness of aid, including Collaborating, Learning, and Adapting, Thinking and Working Politically, Doing Development Differently, Market Systems Development, Conflict-Sensitive Programme Management, and Science of Delivery. Since generic AM approaches have existed for decades in other sectors, AM has the potential to act as a neutral ‘bridge language’ that facilitates exchange and learning among the different communities and donors. \n \nThis report is the result of a learning partnership between the Swiss Agency for Development and Cooperation (SDC) and the Institute of Development Studies (IDS). It assesses the relevance of AM to SDC, how it relates to working practices across SDC, and the key challenges and opportunities for SDC. Its process of elaboration involved a literature review on AM, an exploration of AM approaches from several bilateral donors, a series of 6 interviews with SDC staff and partners working in different countries and thematic domains, and a learning workshop at SDC headquarters (HQ), where staff from several SDC divisions reflected on AM and on how to advance the organisation’s capacity for adaptive programming and learning.

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)0.019
metaresearch head score (Gemma)0.025
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: Other · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0020.009
Scholarly communication0.0090.009
Open science0.0050.008
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.121
GPT teacher head0.308
Teacher spread0.187 · 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
GenreOther

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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Citations1
Published2020
Admission routes1
Has abstractyes

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