Bibliographic record
Abstract
This paper describes the experiences in offering data mining (DM) workshops at University of Norbert Wiener (UNW) in Lima, Peru. This educational initiative is a result of a longstanding collaboration between the Faculty of Engineering and Business at UNW and Thompson Rivers University in Canada. The workshops were offered in May 2014 and May 2015, and had three learning objectives: (1) to acquire practical skills (hands-on experience) in DM, (2) to solve problems using computer systems in bioinformatics and engineering, and (3) to learn fundamental DM concepts and techniques simultaneously in two languages: English and Spanish. The bilingual aspect of the DM workshops was very important, because the students had not only the opportunity to learn advanced computer skills, but also to learn complex terminology in two languages. Bilingualism (Spanish, English) is an important growth factor for the Peruvian economy, and students' bilingual fluency in technical terminology is mandatory for their participation in the development and use of advanced technologies, such as, applications of DM in biomedicine and engineering. In this paper, we present how we have met these three educational objectives using methods based on experiential learning, problem-based learning, and bilingual education.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".