Poverty Alleviation through Empowerment with a Focus on Partnership: A Case Study of Iran
Bibliographic record
Abstract
Empowerment in the area of poverty alleviation, beyond being a partnership for improvement or progress of the performance, means accountability for the prioritized needs. As to empowerment, the approaches are mostly confined to motivational level or organizational and institutional reform. Based on this general approach, Iran has also drawn up many empowerment programs and implemented them through Imam Khomeini Relief Foundation (IKRF), the main supporting organization of the country. Pursuing the empowerment projects on the basis of this limited approach has been the source of instability for such projects. Evaluating the empowerment projects of Iran focused on poverty alleviation, developed and embedded in the broad programs of the IKRF, the study aims to identify the components of a pattern of empowerment for poverty alleviation through a social approach. Finally the proposed strategies are formulated based on three centers of active participation of the poor, strengthening inter-organizational relationships and defining an advisory role for supporting organizations to decision-making bodies. The methodology was descriptive-analytical and the data collected through direct observation and interviews with experts and academic elites and collecting questionnaires. Delphi Questionnaire was filled by the sample population in two categories of managers and experts of the IKRF and the clients. The Snowball technique was applied for identifying the size of the sample. The research questions were responded through SWOT analysis.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".