Bibliographic record
Abstract
Based on the pilot study in Fujimoto, Fu, Yao & Nozu (2022), this research aims further to substantiate the hypothesis that four language skills can be applied to ICT skills and that the subjects' improvements in both skills are correlated, from the viewpoint of liberal arts education at professional colleges or universities.First, the questionnaire of this study's main survey was updated by shifting our research focus to subjects' current language and ICT skills and communicative competence based on their self-report.The main survey was conducted three times in 2022, the first time right before Quarter 1 started (in April), the second time at the end of Quarter 1 (in July), and the third time at the end of Quarter 3 (in December).The survey results reveal that about 61% of the subjects reported their common improvements both in language and ICT skills over time.Also, although 82% and 67% of the subjects reported their betterment in ICT and language skills, respectively, statistically there was no significant improvement in their self-reported confidence in both skills over time.Finally, an overall analysis of the survey results further suggests some thought-provoking hints for curriculum amendments at professional colleges and 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".