Factors for Cross-disciplinary Research Collaboration: Experiences of Researchers at the Faculty of Engineering and Built Environment, UKM
Bibliographic record
Abstract
Cross-disciplinary research is a research activity that involves researchers of multiple disciplines in studying new knowledge. Cross-disciplinary research extends beyond simple collaboration to integrate data, methodologies, perspectives and concepts from various fields to understand the basics or find the solution for real world problems. The approach of cross-disciplinary research taken at the Faculty of Engineering and Built Environment (FKAB), in transforming the researcher, is still deem to be at its minimum because there has yet to be a study on unravelling the difficulties and challenges of reinforcing cross-disciplinary research. Furthermore, the absence of a guideline for conducting such research prohibits the researcher to pursue his research into different discipline. The purpose of this paper is to examine the challenge and difficulty factors that contribute to the less than effective cross-disciplinary researches at the FKAB in particular, and in UKM in general. In addition, through the conducted data analysis, a preliminary guideline can be formed, which can then be used as a guide and resource to develop awareness and capability in implementing cross-disciplinary research. The study was conducted using qualitative and quantitative methods. The qualitative method taken was distributing a questionnaire to academicians at the FKAB. Data obtained are then analysed using WinSteps 3.68.2, which is software utilised in Rasch analysis. Overall, results show that the main factor contributing to difficulties in implementing cross-disciplinary research is the need for solid financial funding.
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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 | 0.047 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.026 | 0.011 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.005 |
| 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".