Cooperation, complexity and adaptation: higher education capacity initiatives in international development assistance programmes in sub-Saharan Africa.
Bibliographic record
Abstract
At a time when global relations are characterised by great complexity, uncertainty and inequality, the role of higher education is crucial for a balanced and coherent development strategy, and achievement of the Sustainable Development Goals (SDGs). This is especially true for countries of sub-Saharan Africa, where there is a critical need to generate knowledge that can be used in the service of social and economic development, human rights and climate change adaptation.\n\nThe study concerns itself with that aspect of international development policy and practice which relates to aid-funded capacity development for systems and institutions of higher education, specifically in the sub-Saharan African context. Looking back over a period of thirty years, this study explores the role of higher education capacity as a component of international development assistance programmes to Africa, provided by international finance institutions, and by OECD member states (including Ireland). With reference to testimonies of authoritative informants and unpublished archival material, it examines the historical pathways which have supported aid-funded higher education capacity initiatives (AFHECIs), and their contribution to strengthening sub-Saharan Africa’s higher education systems and to wider societal transformation.\n\nThe underpinning theoretical perspective which has been chosen as the lens through which to view and reflect on this important subject matter is that of Complex Adaptive Systems (CAS) theory, which has been gaining currency as a theoretical prism on topical problems in public management and organisational analysis. The study critically examines the adequacy of the conventional techniques used by bilateral and multilateral donor agencies in assessing what constitutes an effective AFHECI. It finds that farreaching policy decisions in relation to AFHECIs have in the past been heavily influenced by fickle donor proclivities regarding aid priorities and modalities, rather than the deliberative evidence-based policy-making which donor agencies ostensibly espouse.\n\nFinally, the study resolves the long-running ‘ends -v- means’ antinomy in which the discourse on capacity development has long been mired, and concludes that capacity development, when considered as ‘outcome’, rather than merely as instrument, constitutes a public or social good per se, albeit one which becomes discernible only over time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".