Meeting challenges in the face of change: how the newly independent 4-H Ontario is learning to do by doing
Bibliographic record
Abstract
The following research on 4-H Ontario was conducted to display the importance of organizations which represent rural youth in Ontario. The researcher analyzed the process a non-government organization must take when faced with a major decrease in government support and funding. As part of the research process, a detailed literature review on organizational capacity development and a conceptual framework were completed. Data collection was accomplished by conducting semi-structured personal interviews with 4-H stakeholders. A focus group and direct observation activity with 4-H Ontario members were completed as well as a mail out/Internet survey. The following conclusions were determined in this study: (1) 4-H Ontario has successfully maintained its organization throughout its five year transitional period; (2) 4-H Ontario has dealt with the organizational responsibilities and challenges stemming from its independence from OMAFRA in a variety of positive ways, but focus needs to be placed on challenges facing the organization and (3) organizational change experienced by 4-H Ontario has had an impact on the organization's ability to offer capacity development. The results, conclusions and recommendations presented are intended to assist 4-H Ontario in strengthening its organizational capacity development, which in turn will lend to the overall development of capacity for rural youth in Ontario.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.014 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".