Evidence on the Social, Economic, and Environmental Impact of Interventions That Facilitate Bamboo Industry Development for Sustainable Livelihoods: A Systematic Map
Bibliographic record
Abstract
Bamboo’s perceived potential in livelihood development has led to development interventions that aim to strengthen the bamboo industry via activities such as training participants in bamboo management, strengthening institutions, and raising awareness. Using the Campaign for Environmental Evidence’s guidelines, we systematically map the available evidence of the impact of these interventions. The evidence is scattered across peer-reviewed and grey literature, with no universal reporting standards. Search sources for this systematic evidence map include a bibliographic database, CABdirect (now known as CABI Digital Library); a search platform for peer-reviewed literature, the Web of Science Core Collection; a bibliographic database for academic literature on agriculture and related fields, SEARCH by the USDA National Agricultural Library; a public search engine for scholarly literature, Google Scholar; a general search engine, Google; and the websites of 37 organizations, with both proprietary search engines and Google used to search for pdf files. Overall, 36 documents are included in the final review, describing 28 unique interventions from 13 countries. Most evidence is found outside the peer-reviewed literature. Outcomes including income changes, increased participation and engagement, and policy changes are reported, with economic impacts dominating the evidence base. Very little evidence of negative outcomes is found, likely constrained by reporting bias. Reporting on evidence of these interventions is limited, with many interventions being excluded from the database due to a lack of identifiable evidence of outcomes or impact.
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 | 0.027 | 0.110 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.053 | 0.039 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".