Governance and the making and breaking of social-ecological traps
Bibliographic record
Abstract
Many countries in sub-Saharan Africa have areas of significant ecological importance that overlap with pressing development needs and high levels of natural resource dependence.This makes the design of effective natural resource governance and management systems both challenging and critical.In Ghana, this challenge is made more complex by the necessity of connecting formal, state-led systems of governance with Ghana's informal governance systems through which customary authorities exert considerable control over land and resources.We present findings from two multimethod research projects in two regions of Ghana that have significant issues related to resource exploitation and that have experienced extensive management interventions.The goals of the research were to characterize the social-ecological traps from a local perspective, to describe how governance and management structures interact with and relate to those traps, and to discuss the strategies used and challenges encountered when community-based natural resource management initiatives seek to reverse persistent social-ecological traps.In both case studies, participants described persistent cycles of resource dependence, overexploitation, and unsustainable land-use practices, which are exacerbated by illegal logging, intensive agricultural development, and population growth.Findings highlight how natural resource management is constrained by a lack of capacity to implement and enforce state policies, ongoing tension between customary and state institutions, and ambiguity regarding management responsibility and resource tenure.Interventions included targeted governance reform that centred on improving linkages between customary and state institutions, new and nonlocal actors, and complementary investments in capacity building and training.We conclude with a discussion of implications for the design of effective natural resource governance regimes in Ghana and beyond.
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 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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.047 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".