Incentivizing Sustainable Private Sector Investment in Timber Plantations in Myanmar : Policy Options to Encourage Socially and Environmentally Responsible Investment
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
Forestry has traditionally been one of \n Myanmar’s most important economic sectors, generating more \n in export earnings in the period 2010-2018. It is estimated \n that the country will have lost 12 million ha of forest \n between 1990 and 2020 - the third largest absolute forest \n loss of all countries during that period. The government now \n aims to restore or reforest about 884,000 ha on reserved \n forest (RF) and public protected forest (PPF) land under its \n 2016-28 Myanmar reforestation and rehabilitation program \n (MRRP). A range of reforms is needed to encourage private \n sector investment. These include: (i) identification of \n sufficiently large areas of suitable land close to potential \n processing sites or transport infrastructure and planning of \n land-use allocation; (ii) improving the availability of \n information on identified areas and on the process of \n acquiring plantation leases; (iii) streamlining leasing \n procedures and terms and scope of leases, including possible \n private management of state plantations; (iv) simplifying \n regulations on harvest and transport of plantation timber; \n (v) reviewing the suitability of current fiscal incentives, \n including tax holidays; (vi) improving information on areas \n and productivity of established plantations; and (vii) \n identifying priority research and development needs and \n delivery mechanisms.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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