Bibliographic record
Abstract
Assistance in forestry has been provided to more than 100 developing countries across a span of more than a half-century. The delivery channels for this aid, through the Canadian International Development Agency, have remained relatively unchanged over the years. However, the level of funding as well as the nature and scope of the type of support provided continues to evolve. The emphasis in earlier years tended to focus on stand-alone projects aimed at forest industries development and economic expansion. More recently, despite budget cutbacks, priority has been given to improved governance in the sector through institutional strengthening and capacity building. Multi-sectoral approaches are favoured wherein trees and forests play a key role in sustaining the provision of a range of economic, environmental, social and cultural values. Examples of these types of projects in all parts of the world are described. The strong comparative advantage enjoyed by Canada in the sector positions CIDA to pursue with partners and other donors a number of new directions in addressing pressing forest management and conservation issues in the South. Key words: development assistance, CIDA, IDRC, agroforestry, poverty alleviation, food security, deforestation, community forestry, national forest programs, international.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.003 |
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".