Bibliographic record
Abstract
WIRING SCHOLARLY NETWORKS Rapid developments in computer-mediated communication are associated with a paradigm shift in the ways in which institutions and people are connected. This is a shift from being bound up in small groups to surfing life through diffuse, variegated social networks. Although the transformation began in the pre–Internet 1960s, the proliferation of the Internet both reflects and facilitates the shift. Much social organization no longer fits a group-centric model of society. Work, community, and domesticity have moved from hierarchically arranged, densely knit, bounded groups to social networks. In networked societies, boundaries are more permeable, interactions are with diverse others, linkages switch between multiple networks, and hierarchies are flatter and more recursive. People maneuver through multiple communities, no longer bounded by locality. They form complex networks of alliances and exchanges, often in transient virtual or networked organizations (Bar & Simard, 2001). Workers – especially professionals, technical workers, and managers – report to multiple peers and superiors. Work relations spill over their nominal work group's boundaries and may even connect them to outside organizations. In virtual and networked organizations, management by network has people reporting to shifting sets of supervisors, peers, and even nominal subordinates (Wellman, 2001). How people learn is becoming part of this paradigm shift. There has been some movement away from traditional classroom-based, location-specific instruction to online, virtual classrooms. There has also been some movement away from teacher-centered models of learning to student-centered models and flatter hierarchical relations. Physically dispersed learning is part of this shift.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".