Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the rapid advancement of artificial intelligence (AI), new technology is reshaping business landscapes and creating unprecedented opportunities for innovation and growth. Professors Babu George and Ontario Wooden explore the transformative journey of African American entrepreneurs as they navigate the evolving AI era. Beginning with a conceptual analysis based on firsthand experiences with African American business leaders and supported by the existing literature, George and Wooden trace the historical trajectory of African American entrepreneurship, highlighting the resilience and ingenuity that have long defined this community. They examine critical aspects such as policy frameworks, financial strategies, educational initiatives, and the importance of networking within the AI ecosystem, underscoring the need for inclusive policies and accessible resources to ensure African American entrepreneurs can fully participate in and benefit from the AI revolution. At the heart of the discussion is AI’s role in bridging socioeconomic divides, equipping African American entrepreneurs with powerful tools to overcome systemic barriers and advance toward economic empowerment. Through micro-case studies, innovative insights, and actionable strategies, the book brings home the hope that digital divides and systemic inequities can be overcome successfully. AI Empowered concludes with a forward-looking perspective, envisioning a future where African American entrepreneurs lead AI-driven innovation. It calls for a collective effort to support these trailblazers and foster a diverse, dynamic entrepreneurial landscape that reflects the promise of the digital age.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 it