What role for multi-stakeholder partnerships in adaptation to climate change? Experiences from private sector adaptation in Kenya
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Amidst increasing interest in multi-stakeholder partnerships (MSPs) in climate discourse, this paper identifies four rationales for why MSPs may be particularly suited to supporting adaptation from existing literatures. With a focus on MSPs that seek to support adaptation among micro, small and medium enterprises (SMEs) in Kenya, we then investigate the extent to which this potential is being realised in practice, through interviews with partners engaged in the design and implementation of MSPs. This allows us to examine some of the opportunities, challenges and distributional risks that may result from employing MSPs to support adaptation. We find that through action and investment from donors and the public sector in areas such as research, data access, relationship building, training and capacity building, access to finance and business incubation, MSPs can enable a wide range of private sector actors to deliver adaptation resources to SMEs. Beneficiaries include small-scale SMEs in agricultural value chains in remote regions, that could otherwise fall outside of market inclusion. As such, respondents in this research typically considered MSPs to present an exciting opportunity to plug gaps in adaptation and development finance. Further analysis, however, suggests that dependence on market mechanisms for delivering adaptation resources means that MSPs risk excluding the poorest groups, exposing businesses to new risks and reproducing existing inequalities. Additionally, MSPs often remain heavily dependent on donor-led organisations for both resources and momentum. In Kenya, opportunities to develop more integrated responses to supporting the adaptive capacity of SMEs are being missed through a disconnect between the practice of MSPs and national public sector development frameworks and institutions.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it