Serving Edmonton Heights “Connecting a Low-to-Moderate Income (LMI) Community to Higher Education”
Bibliographic record
Abstract
Serving Edmonton Heights "Connecting a Low-to-Moderate Income (LMI) Community to Higher Education"Impoverished communities are experiencing challenges in acquiring the necessary resources (i.e.financial, knowledge-based, physical, food, etc.) to survive.Many low-tomoderate income (LMI) communities that have been ravaged by drugs, crime, poor education systems, lack of access to healthcare and violence are often located in urban settings near colleges and universities.The connection between institutions of higher education and adjacent, impoverished neighborhoods need to be made in order to improve the social, academic and financial levels of attainment of those families residing in these communities.The bridge that is established between the two will improve the lives of those who living in poverty.At Alabama Agricultural & Mechanical University (AAMU), Prof.Joseph A. Lee initiated a community-based participatory research program (CBPRP) that was created to connect faculty, staff and students in civic engagement initiatives that implemented service-learning programs to a LMI neighborhood located adjacent to the campus.Faculty and students collaborated in designing and implementing service-learning research projects that addressed local needs and issues within this specific neighborhood.The targeted area that was chosen for action and research was the Edmonton Heights neighborhood.Tyler K. Pearson is a first year graduate student at Alabama A&M University pursuing and Masters in Urban and Regional Planning (M.U.R.P.) specializing in Transportation Planning.Currently, I am an AmeriCorps VISTA Volunteer for the Huntsville Network for Urban School Renewal, Inc. (HNUSR, Inc.).The organization is an education non-profit dedicated to raising academic achievement of students residing in low-income neighborhoods and attending Title I Schools.Vista has changed his goal.He plans to pursue a Ph.D. in Higher Education Leadership and Policy to build and develop institutions of higher learning that include in their missions and core values service-learning and community engagement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.172 | 0.017 |
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".