Bibliographic record
Abstract
Addressing complex societal problems requires the collaboration of diverse academic and non-academic partners ideally through transdisciplinarity. Most academic faculty members are couched in their disciplinary mind set with more recent transitions to multi- and interdisciplinarity. Transdisciplinary thinking is the new kid on the block. Switching through mono, multi, and inter to transdisciplinarity will require a paradigm shift. Each is discussed in this paper, so people can more readily find themselves, clarify the nature of their work, and appreciate the magnitude of change required if they shift paradigms. The paper then (a) identifies traits of a transdisciplinary individual who can employ a transdisciplinary orientation, (b) addresses paradigm shifts at the collective and individual levels, (c) discusses how to deal with natural resistance to losing one’s familiar way of thinking and (d) concludes with touchstones and safety-nets that encourage people to take the leap of faith required to embrace transdisciplinarity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.056 |
| Scholarly communication | 0.027 | 0.030 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".