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Record W3090315305 · doi:10.5539/jsd.v13n5p67

Institutional Barriers to Climate Change Adaptation in Burkina Faso: How could We Go around Them?

2020· article· en· W3090315305 on OpenAlexvenueno aff
Fiacre Basson, Djibril S. Dayamba, Joel Korahire, Jean Marie Dipama, François Zougmoré, Tiga Neya

Bibliographic record

VenueJournal of Sustainable Development · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsnot available
Fundersnot available
KeywordsNapAdaptation (eye)BusinessProcess managementClimate changeOperationalizationEnvironmental resource managementDocumentationProcess (computing)MandatePolitical scienceComputer scienceEconomics

Abstract

fetched live from OpenAlex

Despite the existence of a National Adaptation Plan to climate change (NAP) in Burkina Faso, operationalizing adaptation still face a number of challenges. The current study focused on identifying institutional barriers to the strategic objectives of climate change adaptation (CCA) using a literature review and semi-structured interviews conducted with key stakeholders / resource persons involved in the implementation of the NAP. The results revealed a weak collaboration between the NAP steering institution and the ministerial departments covered by the NAP. This situation, first, hampers the implementation of adaptation actions and secondly, the monitoring reporting and verification of adaptation initiatives. Further, the analysis revealed that lack of financial resources poses constraints to many actions that were to be taken by the steering institution and therefore creates poor ownership of the NAP by the main stakeholders that should be actively involved in the NAP process. To cope with the various constraints, it is necessary to have strong political support in many aspects. For instance, it was judged that institutionalizing the role of climate change (CC) focal point within the ministries and embedding NAP monitoring and evaluation (M&E) objectives and indicators with existing functional M&E systems in the sectorial ministries will ease CCA actions integration in operational plans, their implementation and documentation. Moreover, it is relevant to have a continuous capacity building plan to keep stakeholders updated on climate change issues as this will support them in their mandate of mainstreaming CC into ministerial operational plans and lead to optimal CCA implementation and monitoring.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.048
GPT teacher head0.244
Teacher spread0.197 · 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.

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

Citations11
Published2020
Admission routes1
Has abstractyes

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