The contribution of capacities and context to EIA system performance and effectiveness in developing countries: towards a better understanding
Bibliographic record
Abstract
EIA has the potential to contribute towards more sustainable development through well-informed decision-making. Evaluation studies conclude that this potential is utilised to a considerable extent in rich western democratic countries such as Canada and The Netherlands, but hardly in developing countries. EIA capacity development programmes have not been able to structurally change this situation in developing countries, where there is lack of insight into the root causes of low EIA performance. There is growing evidence that context-specific characteristics such as the political system and the capacities of the key stakeholders are insufficiently considered in evaluations of EIA system performance. Most evaluations focus primarily on procedural shortcomings. As a consequence, capacity development activities that arise from EIA system evaluations do not tackle the underlying constraints. The aim of this article is to identify factors influencing EIA system performance in developing countries, and a conceptual model was developed to provide insight into those factors, building on a review of the current approaches and insights. A thorough assessment of EIA system performance is considered a necessary first step before capacity development activities can be identified, aiming to develop EIA systems that utilise the potential for EIA in a country.
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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 | 0.023 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.008 |
| 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".