Six reasons to integrate arts and sciences in higher education
Bibliographic record
Abstract
Integrating arts and sciences in higher education is a compelling challenge for students, instructors, and institutions (NAS 2018). However, even skilled practitioners find interdisciplinarity difficult for epistemological, cultural, and linguistic reasons (Mather et al. 2023). The same difficulties confront educators and students. Nevertheless, the effort is joyfully worthwhile for six key reasons. First, arts–science interdisciplinarity is needed. Addressing today's wicked problems at the interface of biology and society demands interdisciplinary competence (e.g., Renshaw and Valiquette 2017, Sanborn and Jung 2021) and associated twenty-first-century skills (Pellegrino and Hilton 2012, WEF 2023). Fostering in students the capacity to appreciate, question, and connect multiple learning paradigms is invaluable for their future success. Second, interdisciplinary competence can enhance intercultural competence. Both require the transferable skills of empathy, respect, humility, and effort (Fraser and Schalley 2009, Paracka and Pyn 2017, Islam and Stamp 2020). Furthermore, science–arts initiatives can deliberately enhance diversity, inclusion, and decolonization by breaking down disciplinary boundaries and broadening exposure to other fields (NAS 2018, Rigby 2020; cf. Clark et al. 2020).
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".