Bibliographic record
Abstract
The premise of this chapter is that Innovation Growth is tightly tied to the collaborative process of socializing knowledge. Case examples from leading companies leading the way in socializing knowledge leading practices will be profiled. These companies will be a mix of new stories from a mix of both profit and not for profit organizations, in a mix of industries. The leaders of these organizations recognize that the socialization process of knowledge is core key to innovation growth. This chapter tells the story of change agents that are helping to move from vision to execution successfully. You will hear of experiences where the full enablement of their programs are not fully funded, or necessarily aligned across all levels of management where the generational gaps between understanding community and value network networks vs those based on linear “one way flow” models continue to conflict with one another; The case studies all started off with a small project well scoped and defined, and organically evolved vs a big bang approach. Each of these cases is rooted in a clear business need either for employee engagement or customer engagement needs.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".