Multidisciplinarity, interdisciplinarity, and transdisciplinarity in health research, services, education and policy: 3. Discipline, inter-discipline distance, and selection of discipline
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.
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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 | 0.051 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 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".