Redbird Buzz Episode 35: Bunmi Akinnusotu, M.S. '06, February 20, 2024
Bibliographic record
Abstract
Illinois State University alum Bunmi Akinnusotu, M.S. '06, is no stranger to the intricacies of foreign policy. From her childhood as a first-generation American listening to her Nigerian family discuss world politics and cultures, to her experience as a graduate student working with Illinois State's Stevenson Center and AmeriCorps, Akinnusotu has a passion for studying and understanding cultures and affecting policy on a global level. Akinnusotu's career has spanned nonprofits, government agencies including the U.S. Environmental Protection Agency, and higher education. Her current role as the director of city innovation for the Aspen Institute is a culmination of her career and education as she works to engage urban policymakers both here in the United States and around the world. In this episode, Akinnusotu discusses how significant impacts can be made both on a global and individual level, shares why she launched her own podcast, and speaks about what she learned through her work with the Stevenson Center at Illinois State.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.228 | 0.061 |
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".