Bibliographic record
Abstract
There are increasing calls for academics to abandon "traditional" disciplinary research and to engage in multi-, inter- and transdisciplinary research. The argument is that this will serve to break down working in "silos" and somehow lead to more innovative research. This article examines the concepts of multidisciplinary, interdisciplinary and transdisciplinary research to determine if this kind of research is possible in legal research. The basic premise is that science is unified by the need for some kind of justification, arguably in the form of falsifiability of theories. But science is also divided into natural, social and human sciences and this article argues that this division is based on methodological differences. Whilst the natural sciences employ a mostly empiricist methodology and the human sciences employ a mostly rationalist methodology, the social sciences seem to employ a mixture of the two methodologies. Law is a human science and moreover a professional discipline. Some argue that this professional nature militates against multi-, inter- and transdisciplinary (MIT) research as it requires law students to be taught how to "think like a lawyer". The article concludes that most law researchers engage in multidisciplinary research on a regular basis, but that interdisciplinary research is highly unlikely and transdisciplinary research almost never happens.
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 | 0.290 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.011 | 0.131 |
| Scholarly communication | 0.030 | 0.039 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".