Empowering Quality Excellence: A 10-Day Quality Engineering Boot Camp for Accelerated Learning
Bibliographic record
Abstract
Jakia Sultana, currently a Ph.D. candidate in Teaching, Learning, and Culture with a focus on STEM education, is also serving as a Research Associate dedicated to enhancing the educational journey of minority students in engineering fields.Her research is centered on developing and integrating effective methodologies within engineering education to improve teaching and learning practices, particularly for minorities.By identifying and implementing innovative strategies, she aims to seamlessly incorporate engineering education into curricula, thus elevating the academic experience for minority students in diverse settings.Jakia's work is characterized by a unique blend of passion and insight, drawing from her academic and research background to enrich the engineering education discourse.Her commitment lies in pushing the boundaries of traditional education to foster a more inclusive and understanding environment for minority students in engineering disciplines across U.S. universities.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".