Facilitating transdisciplinary sustainable development research teams through online collaboration
Bibliographic record
Abstract
Purpose The purpose of this paper is to discuss the potential of online communication technologies to facilitate university‐led transdisciplinary sustainable development research and lower the ecological footprints of such research projects. A series of case studies is to be explored. Design/methodology/approach A one year project is conducted in which a series of research tasks are carried out on an online communications platform. Findings are compared to other examples from the literature. Findings Online communication technology can be used to facilitate transdisciplinary research tasks, saving time, money and with less environmental impact than that of face‐to‐face meetings. However, in order for online collaboration to be successful the researchers must be very organized and have strong facilitation skills. Research limitations/implications The research takes place in a North American setting. Time zone issues and access to sufficient internet technology can be a barrier in global research collaboration. Practical implications Online communication technology can be a practical way to lower the environmental impact of the research process and lower the cost of collaborative meetings. Originality/value The outcomes of this research suggest online collaboration can play a much larger role in student and faculty research, including but not limited to online research analysis, data collection and field exploration.
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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".