Bibliographic record
Abstract
Modeling of virtual organization (VO) can be a useful method of making sense of a plethora of organizations that are proclaimed to be “virtual,” “virtualized,” or to exhibit “virtualness.” Since the advent of these notions (Byrne, 1993; Davidow & Malone, 1992; Mowshowitz, 1994), an enormous proliferation of VOs has followed in theory and practice across academic disciplines and industries. Being “virtual” had almost become a fashion embraced by corporations and other businesses, groups of organizations engaged in cooperation/collaboration or trading, libraries, schools, government organizations, non-government organizations, churches, museums, and so on. The implication of these developments is that it has become difficult to reach an agreement on what VO is beyond the customary agreement at a lexical level. Lexically, the virtual character refers to a potentiality and effect that divert from the actual appearance of a virtual thing (Webster, 1988). Thus, a VO is an effect of interaction of what in fact are different organizations or constituents of organizations (groups and individuals). Introduced by inventors of VO, this axiom has remained undisputed to date.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 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".