Hyperloop transportation technologies: practices for open organizing across VUCA contexts
Bibliographic record
Abstract
Abstract Open organizations are structures in which members of the public engage in work for the organization. Examples include open-source software, Amnesty International, Wikipedia, and Lego communities. Much research focuses on structural design characteristics of open organizations, such as pre-specified task divisions and integration teams. These practices require the organization to a priori structure in response to its mission. Increasingly, however, open organizations like CrowdDoing and Hyperloop Transportation Technologies (HyperloopTT) require public involvement across volatile, uncertain, complex, ambiguous (VUCA) contexts. These open organizations must respond to changing political, competitive, and socio-economic events. Structural clarity is more difficult, and contributors may participate in the creative development of new technologies, new policies, and new sources of funding. Working from practices supporting participant engagement in more stable environments, we qualitatively observe HyperloopTT to understand internal practices for open organizing in more VUCA contexts. We observe four practices allowing for the flexibility, versatility, and accommodations needed for open organizing in such settings. The HyperloopTT practices allow more porosity and self-determination—not simply in how people divide and integrate tasks, but also in the exploration and experimentation of the work itself. More than task workers, we see a new class of open organizing participants: creative work designers.
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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".