Building a Foundation for Success in Natural Resources Extension Education: An International Perspective
Bibliographic record
Abstract
The practice of extension in improving forest and natural resource management around the world is increasing. In 2003 an international symposium of extensionists working in forestry and natural resources was convened in Troutdale, Oregon. Thirty-five papers from 11 countries focused on successful strategies that have been employed in extension work around the globe. Twenty-two strategies were highlighted that relate to educational approach or programming. Members of the Extension Working Party of the International Union of Forest Research Organizations (500 members from 70 countries) were surveyed to see if they used these strategies often or sometimes, or if they did not use them but would like to, or if they don’t feel the strategy is relevant to them. Results were compiled by three constructed regions: U.S. and Canada; Europe and Australia; and Asia, Africa, and Latin America, and differences were tested using Pearson’s χ 2 . In general, the strategies were consistently and widely used around the world. Typically from 70 to 95% of the survey respondents indicated that they use the strategy often or sometimes. Regional differences occurred with only six of the 22 strategies. For example, the strategy develop collaborations with associations of learners, such as forest owner associations was used often or sometimes by 90% of respondents from the U.S. and Canada, 83% of respondents from Europe and Australia, and only 47% of respondents from Asia, Africa, and Latin America. Reasons for these significant (p < 0.0001) differences are proposed. Additionally, some barriers to use of some of the strategies are reported.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".