Developing Leadership Competencies to Promote the Community Development of the Mississippi Delta Region
Bibliographic record
Abstract
Parts of the Mississippi Delta region have been known for a number of very serious problems, including poverty, illiteracy, race relations, public health, and declining economy issues. An approach to combat its social and economic ills is to develop leadership competencies through a community leadership development initiative. The purposes of this study were to assess the extent to which leadership skills were developed in the Mid-South Delta Leaders (MSDL) program participants as compared to non-participants and to determine the specific impacts of the program on its participants. The study used a mixed methodology to assess the effect of the program on the development of a number of leadership competencies. The quantitative part used a posttest-only design with non-equivalent groups. The Leadership Practices Inventory (LPI-self) instrument was used to measure the leadership actions and behaviors. Data were analyzed with MANCOVA test to examine the effectiveness of the community leadership development initiative. Results suggested that the participation in the MSDL program significantly influences the leadership competencies with an F = 2.554, p < 0.05. The qualitative part was to explore using a follow-on open ended question: what specific impacts or changes did the MSDL program have on you? The results identified ten themes of impact. These themes of change range from better understanding of cultural and diversity differences, better networking and collaborative opportunities, promotions and better jobs, and better understanding how leadership makes an impact on community change. Key words: community development, community leadership development initiative, leadership competencies, leadership practice inventory
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".