Multidisciplinarity, interdisciplinarity, and transdisciplinarity in health research, services, education and policy: 3. Discipline, inter-discipline distance, and selection of discipline
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND/PURPOSE: Multiple disciplinary efforts are increasingly encouraged in health research, services, education and policy. This paper is the third in a series. The first discussed the definitions, objectives, and evidence of effectiveness of multiple disciplinary teamwork. The second examined the promoters, barriers, and ways to enhance such teamwork. This paper addresses the questions of discipline, inter-discipline distance, and where to look for multiple disciplinary collaboration. METHODS: This paper proposes a conceptual framework of the knowledge universe, based on a review of a number of key papers on the Global Brain. These key papers were identified during a literature review on multiple disciplinary teamwork, using Google and MEDLINE (1982-2007) searches. RESULTS: A discipline is held together by a shared epistemology. In general, disciplines that are more disparate from one another epistemologically are more likely to achieve new insight for a complex problem. The proposed conceptual framework of the knowledge universe consists of several knowledge subsystems, each containing a number of disciplines. The inter-discipline distance can guide us to select appropriate disciplines for a multiple disciplinary team. CONCLUSION: If multiple disciplinarity is called for, the proposed view of the knowledge universe as a series of knowledge subsystems and disciplines, and the place of health sciences in the knowledge universe, will help researchers, practitioners, and policy makers to identify disciplines for multiple disciplinary efforts.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.026 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it