Managing the Miombo Woodlands of Southern Africa : Policies, Incentives and Options for the Rural Poor, Volume 2. Technical Annexes
Bibliographic record
Abstract
Miombo woodlands stretch across Southern \n Africa in a belt from Angola and the Democratic Republic of \n Congo (DRC) in the west to Mozambique in the east. The \n miombo region covers an area of around 2.4 million km. In \n some areas, miombo has been highly degraded as a result of \n human use (southern Malawi and parts of Zimbabwe), while in \n others, it remains relatively intact (such as in parts of \n northern Mozambique, and in isolated areas of Angola and the \n DRC). From a conventional forester's perspective, \n miombo is fundamentally uninteresting. It supports \n relatively few good commercial timber species. The \n management of commercial species has been problematic. The \n best areas were logged over long ago. Except in a few areas, \n remaining commercially viable stocks are relatively small \n and difficult to access. Public forestry institutions have, \n for the most part, failed to put in place effective \n management systems for forests, preferring instead to limit \n their role to regulation and revenue collection, rather than \n to management per se. The objectives of this paper are \n threefold, and the paper is structured around these \n objectives. First, in section two, the paper describes some \n of opportunities for improving the use and management of \n miombo woodlands. Second, in section three, outline some of \n the barriers which are preventing households, communities, \n and countries from adopting better and more sustainable \n woodland management practices. In section four, by exploring \n some of the policy opportunities for removing these \n barriers, with the objective of strengthening miombo's \n contribution to reducing risk and vulnerability of poor \n rural households through sustainable forest management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.008 |
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".