Application of the theory-driven approach to evaluation in program planning
Bibliographic record
Abstract
The present practicum with the Manitoba Healthy Child Initiative involved the development of an implementation plan and evaluation plan with an established interorganizational committee comprised of senior managers from education, social services and mental health. The proposed program is a multi-faceted education-based program for children under the age of twelve with severe emotional and behavior disorders currently not attending school. The use of the theory-driven approach during planning is undocumented and the literature and the practicum offered the unique experience to apply this approach at the stage of program planning. The activities of the practicum included reviewing the literature related to the program's proposed interventions; developing program outcomes; articulating the program's theory, based on the literature and the implicit logic model of the committee members; and the creation of an evaluation framework. The temporal replacement of evaluation planning to the program planning stage, inparticular the application of the theory-driven approach at this stage has implications to evaluation use theory and the role of the evaluator. (Abstract shortened by UMI.)
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 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 | 0.125 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".