Bibliographic record
Abstract
Abstract: Empowerment evaluation (EE) is the use of evaluation concepts, techniques, and findings to foster improvement and self-determination. It employs both qualitative and quantitative methodologies. Although it can be applied to individuals, organizations, communities, and societies or cultures, the focus is usually on programs. A wide range of programs use EE, including substance abuse prevention, indigent health care, welfare reform, battered women’s shelters, adolescent pregnancy prevention, individuals with disabilities, doctoral programs, and accelerated schools. This approach has been institutionalized within the American Evaluation Association since its introduction in 1993 and is consistent with the spirit of the standards developed by the Joint Committee on Standards for Educational Evaluation. Empowerment evaluation has become a worldwide phenomenon, its acceptance in part a function of timing. Evaluators were already using forms of participatory self-assessment or were prepared to use it because it represented the next logical step. Funders and clients were focusing on program improvement and capacity building. A critical match between people and common interests was made with an underlying and often implicit commitment to fostering self-determination. Widespread use of this evaluation approach was also a result of its appearance on the Internet.
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 | 0.117 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.010 | 0.027 |
| 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".