Bibliographic record
Abstract
This chapter describes a multi-year study on how online collaboration could be effective in the design of a research equipment sharing lab with university and industry partners. More specifically, this chapter focuses on the “soft” challenges of how to establish a shared vision of the project goals, trust among diverse partners, most of whom had never collaborated before this project, and the different leadership model that was required for effective online interactions. Internal obstacles included differences between academic and industrial organizational cultures (publication/patent tension) and the difficulty in establishing trust through online interactions. External obstacles included differences in leadership style (commanding vs. collegial agreement). While there were also logistical issues in the actual sharing of resources, the focus was on online interactions that crossed disciplinary boundaries (amongst university partners) and competition (amongst industrial partners). Collaboration models were useful in assessing online collaboration effectiveness.
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 | 0.024 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.028 | 0.042 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".