Using evaluation to improve program quality based on the BELL model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it