Evidence on the social, economic, and environmental impact of interventions that facilitate bamboo industry development for sustainable livelihoods: a systematic map protocol
Bibliographic record
Abstract
BACKGROUND: Bamboo has been identified as a potential instrument for socioeconomic development due to its fast growth, perceived environmental benefits, promising material properties, myriad applications, and relative underdevelopment as a global industrial product. Many projects and interventions have been carried out that aim to utilize bamboo's social and environmental potential in development. However, critical evaluations that demonstrate this effect using real-world evidence and outcomes are rare, and existing case studies have not been collated and analyzed in a systematic way. The proposed systematic map aims to summarize and evaluate evidence on the social, economic, and environmental impact of bamboo industry development initiatives on beneficiary communities and ecosystems, and to identify priority areas for future funding and research. METHODS: In the proposed systematic map, we will collect and thematically categorize evidence on the social, environmental, and economic impact of bamboo development implementations, identifying themes, research gaps, and critical success factors. Literature discussing this type of intervention is published by researchers, organizations, and governments in academic journals, institutional reports, and program evaluations describing various socio-economic and environmental outcomes, impacts and metrics for success. Search sources for this systematic map therefore include bibliographic databases, institutional websites, web-based search engines, and expert consultation. Targeted search strings will be used to identify relevant texts in a two-step review process comprising an abstract and a full-text screening process. Sources describing interventions with a primary aim of bamboo industry development for social benefit that concluded between 1990 and 2021 will be sought. Metadata coded from these texts will be reviewed, categorized, and checked by two reviewers. Reviewers will be checked for consistency on batches of 30 articles using the Kappa interrater reliability test with a goal of a Kappa coefficient of 0.9. Metadata will be coded into different categories including outcomes and impacts using NVivo. Results of both quantitative and qualitative data analysis will be summarized in a searchable online database. Themes will be synthesized and explored in a narrative review and using simple logic models demonstrating theories of change for eligible case studies.
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 | 0.117 | 0.147 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.032 | 0.019 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 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".