Bibliographic record
Abstract
Mouna Shaker was an administrator and staff member in the area of grants and sponsored programs at the American University in Cairo from the 1980s to 2010s, and had been an undergraduate student at AUC in the early 1980s. She recalls her time as a student science major, and working for AUC's Desert Development Center managing grants; the DDC's personnel, projects, and funding are covered. Shaker recounts her work for AUC's grants office, later renamed Office of Sponsored Programs (OSP) through the 1990s and2000s, describing its directors and how its procedures developed and became increasingly formalized, for example through more comprehensive documentation. Shaker tells about serving as Director of OSP from 2006 to 2011, and explains its relationship with the Development Office, and mentions AUC units highly reliant on grants like the Social Research Center. She observes how grant sponsors' requirements became increasingly complex over time, and notes the growth in the volume of grants received by AUC especially as Provost Lisa Anderson pushed for increased faculty research after 2008. Shaker discusses significant grant sponsors, including government agencies like Canada�s IDRC and American USAID (in supporting AUC new campus construction and programs like the Leadership for Education and Development[LEAD] student scholarships, and initiatives aided by the American Schools and Hospitals Abroad [ASHA] program). She also covers key private foundation support from the Ford Foundation, Mellon Foundation, and the collaboration with Saudi Arabia's King Abdallah University of Science and Technology (KAUST). Shaker also speaks about the transition to the New Cairo Campus.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.225 | 0.103 |
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".