Virtual collaboration in the built environment
Bibliographic record
Abstract
Throughout the 2013 to 2014 academic year, three institutions have been collaborating in the education of three cohorts of students through the BIM Hub project ; these are Coventry University and Loughborough University in the UK and Ryerson University in Canada. Students formed groups of six individuals, two from each university, including architects, construction engineers and project managers. The project was designed to create an authentic simulation of industrial collaboration and practices. At Coventry participation was optional (students had the alternative of forming collaboration with other Coventry students). At Ryerson and Loughborough participation was mandatory. They were set a project to design and plan a building for a particular site in Coventry through forming online collaboration, and reflect on their experiences. The study was funded by the Higher Education Academy in the UK with the intention of identifying which success factors led to effective online collaboration and is a follow-up to a previous project sponsored by the Hewlett Packard Catalyst Program (Soetanto, et al, 2014). Focus groups were conducted with the students at the institutions, the following analysis focuses on the issues faced and solutions identified in terms of the technologies involved and the strategies for successful collaboration. The analysis focuses on two of the universities and offers reflections based on their experience.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".