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Record W2115621988 · doi:10.5191/jiaee.2006.13303

Building a Foundation for Success in Natural Resources Extension Education: An International Perspective

2006· article· en· W2115621988 on OpenAlexaboutno aff
James A. Johnson, Janean Creighton, E. R. Norland

Bibliographic record

VenueJournal of International Agricultural and Extension Education · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsFoundation (evidence)Perspective (graphical)Extension (predicate logic)Natural resourceNatural (archaeology)Political scienceComputer scienceGeographyArchaeologyArtificial intelligence

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.286
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2006
Admission routes1
Has abstractyes

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