The Digital Equity Leadership Lab (DELL)
Bibliographic record
Abstract
This paper presents the Digital Equity Leadership Lab in Baltimore, Maryland as a case study of community leadership development to promote digital equity and justice. While several studies of community leadership development exist, few are focused on its role in promoting digital equity and justice. This case study attempts to address this gap in the scholarly literature through the following research question: How might DELL serve as a community-based leadership training model to develop the next wave of digital equity leaders? Through our analysis of interviews with community leaders, outside experts, and community foundation staff, we discovered the following three main findings: (1) bringing national policymakers and advocates together with community leaders is powerful and transformative; (2) digital inequality is a social, not a technological problem; and (3) community leaders need access to a shared platform and to each other to create change. These findings suggest that community leaders can benefit from seeing their work within a digital equity ecosystems framework, which calls attention to the importance the interactions that exist among individuals, populations, communities, and their broader sociotechnical environments that all shape the work to promote more equitable access to technology and social and racial justice. This case study report concludes with recommendations for community leaders, including community foundations, working to uncover systemic discrimination shaping digital inequality today to advance digital equity and justice.
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 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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".