Using evaluation to improve program quality based on the BELL model
Bibliographic record
Abstract
Building Educated Leaders for Life (BELL) is a national not-for-profit organization whose mission is to increase the educational achievements, self-esteem, and life opportunities of elementary school children living in low-income urban communities. BELL has been engaged in formal evaluation, internally and externally, for more than five years and has built internal evaluation capacity by investing in a specialized full-time evaluation team. As part of a continuous program improvement model of evaluation, BELL uses the data to refine program implementation and replicate successful elements of the services and operations. In this chapter, the authors highlight best practices from the field by outlining BELL's approach to using evaluation data for continuous program improvement. Key strategies include (1) carefully identifying intended users of the evaluation throughout the organization and among its external stakeholders, then working closely with intended users throughout the evaluation process, ensuring full engagement at every step of the process; (2) reporting findings in a readable, user-friendly format and timing the reporting so that it is aligned with programmatic decision making and planning cycles; and (3) making and supporting explicit recommendations for the next program cycle, where intended users have agreed to recommendations and ownership is assigned. BELL's successful use of data for improvement is evidenced by the consistently strong outcomes for the students it serves as well as increased efficiency and satisfaction related to service delivery that has supported the replication of BELL's programs nationally.
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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 | 0.316 | 0.335 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".