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Record W6983442009

Meeting challenges in the face of change: how the newly independent 4-H Ontario is learning to do by doing

2006· dissertation· en· W6983442009 on OpenAlexaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2006
Typedissertation
Languageen
FieldArts and Humanities
TopicArchaeology and Historical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsVariety (cybernetics)Focus groupOrganization developmentGovernment (linguistics)Face (sociological concept)Process (computing)Independence (probability theory)Capacity building
DOInot available

Abstract

fetched live from OpenAlex

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.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.885

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.221
Teacher spread0.169 · 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 designQualitative
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

Citations0
Published2006
Admission routes1
Has abstractyes

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