Measuring community empowerment: a fresh look at organizational domains
Bibliographic record
Abstract
In 1986, the Ottawa Charter identified community empowerment as being a central theme of health promotion discourse. Community empowerment became a topical issue in the health promotion literature soon afterwards, though its roots also come from earlier literature in community psychology, community organizing and liberation education. Subsequent international conferences to address health promotion in Sundsvall, Adelaide and Jakarta have acted to reinforce this concept. It is as relevant today as it was more than a decade ago. The literature surrounding health promotion has since moved onto other overlapping theoretical perspectives, such as community capacity and social capital. And yet the critical issue of making community empowerment operational in a programme context remains thorny and elusive. Community empowerment is still difficult to measure and implement as a part of health promotion. This article offers a fresh look at key theoretical and practical questions in regard to the measurement of community empowerment. The theoretical questions help to unpack community empowerment in an attempt to clarify how the application of this concept can be best approached. The practical questions address the basic design characteristics for methodologies to measure community empowerment within the context of international health promotion programming. The purpose of this article is to allow researchers and practitioners to address again the important issue of making community empowerment operational.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".