Developing the Program Evaluation Framework for Investment Agriculture Foundation of British Columbia
Bibliographic record
Abstract
This thesis aimed to gain an in-depth understanding of effective program evaluation frameworks, particularly in the agriculture sector. Specifically, the analysis was focused on assessing monitoring and evaluation of public and private sector projects to assist the British Columbia Investment Agriculture Foundation (IAF), the client for this thesis, in identifying smart practices as a way to support constant improvement in their organization. This research involved conducting a literature review of the most recent and relevant literature on program evaluation, particularly works that related to nonprofit organizations in the agriculture sector, interviewing IAF staff, developing a jurisdictional scan of program evaluation frameworks in the Netherlands and New Zealand, and conducting a review of existing IAF evaluation documents to identify and discuss key themes for an effective program evaluation framework and provide examples of smart evaluation practices that may be adapted by IAF. The recommendations include integrating formative and summative evaluation practices, developing targeted programs with well-defined key performance indicators (where possible), and capitalizing on data visualization software for monitoring and reporting on project goals in real-time.
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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 | 0.097 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".