Bibliographic record
Abstract
Collaboration and Social Networking: The Keys to Knowledge Management-Introductory Thoughts Jay Liebowitz Knowledge and Collaboration in Multihub Networks: Orchestration Processes among Clinical Commissioning Groups (CCGs) in the United Kingdom Celine Miani, Markos Zachariadis, Eivor Oborn, and Michael Barrett Religious Communities of Practice and Knowledge Management-The Potential for Cross-Domain Learning Denise A.D. Bedford Cross-Cultural Technology-Mediated Collaboration: Case Study of Oxfam Quebec and Peru Kimiz Dalkir Enabling Knowledge Exchange to Improve Health Outcomes through a Multipartner Global Health Program Theresa C. Norton Emperor: A Method for Collaborative Experience Management Ulrike Becker-Kornstaedt and Forrest Shull Real-Time Knowledge Management: Providing the Knowledge Just-In-Time Moria Levy Building Vertical and Horizontal Networks to Support Organizational Business Maureen Hammer and Katherine Clark Social Network Analysis: A Pharmaceutical Sales and Marketing Application Molly Jackson, Doug Wise, and Myra Norton Collaborating Using Social Networking at Price Modern Gloria Phillips-Wren and Louise Humphreys Visual Knowledge Networks Analytics Florian Windhager, Michael Smuc, Lukas Zenk, Paolo Federico, Jurgen Pfeffer, Wolfgang Aigner, and Silvia Miksch A Framework for Fostering Multidisciplinary Research Collaboration and Scientific Networking within University Environs Francisco J. Cantu and Hector G. Ceballos Knowledge Management and Collaboration: Big Budget Results in a Low Budget World Andrew Campbell and Melvin Brown II TATA Chemicals-Knowledge Management Case Study B. Sudhakar and Devsen Kruthiventi Knowledge-Enabled High-Performing Teams of Leaders Bradley Hilton and Michael Prevou
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.042 | 0.024 |
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".