Application of Analytic Hierarchy Process for Assessing Sustainable Development among Underprivileged Communities
Bibliographic record
Abstract
<p>One of the purposes of sustainable development assessment is to identify the most importance criteria and sub-criteria of sustainable development that have the most significant contribution to the local community. To date, few studies have inquired into qualitative methods to assess these criteria and sub-criteria. In response to this gap in the literature, we propose an application of the analytic hierarchy process (AHP) method to prioritize thirteen sub-criteria of sustainable development among underprivileged community of Setiu Wetlands Terengganu. Consistency ratio and weighted geometric mean are the two important computation steps of the AHP prior to proposing global weights of sub-criteria. The computational results indicate that ‘Education’ is the most important sub-criteria with 15.4 % of global weight. At the other extreme ‘global economic partnership’ is the least important sub-criteria for this group of community. The outcome of the proposed method is a weight of sustainability for all sub-criteria which offers a guide to government in identifying the appropriate action for uplifting the community quality of life.</p>
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".