Best Practices: A Young Professor's Reflections on Higher Education and Democracy for a World Beyond Our Borders
Bibliographic record
Abstract
Dr. Khristina Haddad is an assistant professor who has finished her fourth year in the Department of Political Science at Moravian College. Her teaching philosophy and practice are highlighted in our "Best Practices" feature in this issue of the Journal of College and Character. See http://collegevalues.org/pdfs/Haddad.pdf Having completed her Abitur in Stuttgart, Germany, she fell in love with political theory and the liberal arts at Reed College in Portland, Oregon, and continued her political theory studies at McGill University in MontrÉal, Canada. Dr. Haddad has taught at the University of Latvia in Riga as an instructor for Civic Education Project. In 2003, she graduated from the Political Science doctoral program at the University of Michigan-Ann Arbor. Her research addresses the political importance of how we think about time.
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.018 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.015 | 0.036 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".