Transdeep learning in higher education: Institutionalising transdisciplinarity
Bibliographic record
Abstract
Abstract This think piece was serendipitous. While investigating deep learning and transdisciplinary learning for another paper, a Google Scholar search using transdisciplinary deep learning generated zero results. Transdisciplinary, deep learning yielded nominal results. Neither term was defined or elaborated. This unexpected outcome prompted an intellectual exploration of the import of the comma. We deduced that it matters, coined the neologism transdeep learning and speculated about its conceptualisation. Our thoughts (findings per se) may shape how (if) universities institutionalise transdisciplinarity in their modus operandi. We recommend strategically, sequentially, institutionalising deep learning, then transdisciplinary learning followed by transdisciplinary, deep learning; and, ultimately, transdeep learning. We offer transdeep learning as an avant‐garde, next‐level construct that can inform higher education's strategic planning, academic infrastructure, curricular development, pedagogy, and research within and outside the academy via ties with industry, government, and civil society.
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".