Bibliographic record
Abstract
Forming 16 percent of the global population and growing, the large numbers of youth particularly in the developing world presents both a challenge and an opportunity. Although better educated than their parents, young men and women are chronically unemployed or in vulnerable work positions. While the majority of young people live in rural areas, these issues have sometimes resulted in large scale migration from rural to urban areas. In forested areas, those who remain are often highly dependent on forests for goods and services for their livelihood. Community forestry has been shown to be an effective strategy for sustainable forest management and livelihoods. Unfortunately, youth have often been marginalized in benefiting from or participating in decision-making about community forests. This is frequently attributed to local, cultural, and traditional norms that give priority to older generations in decision-making. Given their stake in sustainable forest management in a post-pandemic world, as well as their large numbers, it is important to utilize new approaches to bring young men and women together with older generations to address challenges and foster opportunities. This will then capitalize on the knowledge, energy, enthusiasm, innovative ideas, leadership ability, technological literacy, and resilience that youth can contribute to community forest management and rural communities.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".