Creating Global Citizens: Exploring Internationalization at HBCUs
Bibliographic record
Abstract
The American Council on Education (ACE) launched Creating Global Citizens: Exploring Internationalization at Historically Black Colleges and Universities (HBCUs) in 2010 to learn how HBCUs are internationalizing their campuses. Conducted as an action-research study, the project’s goals were to (1) identify factors that impede or enhance the internationalization process at HBCUs and (2) disseminate findings to the higher education community. The primary aim of the project was to build on HBCUs’ successes in internationalization by assisting them in being more strategic in advancing initiatives, accelerating and deepening their efforts, and positioning them to pursue funding and partnering opportunities for internationalization.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.017 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".