Taking Evaluation Contexts Seriously: A Cross-Cultural Evaluation in Extreme Unpredictability
Bibliographic record
Abstract
The following paper details the evaluation of a public health education project in the state of Maine. Evaluation of education projects presents a challenge, in that the effects of the intervention are not easy to trace and outside influences difficult to impossible to control. This study approached this difficult issue at the start by focusing on one variable for which data are readily available (namely blood lead testing rates). The evaluation was further enhanced by use of a model called "RE- AIM", which measures the reach, efficacy, adoption, implementation and maintenance of educational projects (Glasgow, et.al. 1999). Measurements centered on data tracked through newly created databases and focused on elements directly attributable to the project (i.e. behavior of medical personnel trained through project activities). Finally, small focus groups and interactions with families served by the program were used to derive qualitative data that provided a broader perspective on the success of activities. As the program ultimately seeks to entirely eliminate childhood lead poisoning, this paper concludes with a discussion of areas that continue to need attention for future education projects.
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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 | 0.518 | 0.515 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".