Practical relevance of knowledge management and intellectual capital scholarly research: Books as knowledge translation agents
Bibliographic record
Abstract
Abstract To enhance our understanding of the relevance of knowledge management/intellectual capital (KM/IC) academic research, this study explores the sources authors utilize to develop their book content. Ten prominent KM/IC book authors were interviewed to identify if and how the KM/IC academic literature is being disseminated through books. It was confirmed that the body of knowledge present in peer‐reviewed journals is utilized in the development of book/textbook content. Thus, books serve as knowledge translation agents through which academic literature is summarized, aggregated, and transformed into a format that may be easily comprehended by non‐academics. In addition to peer‐reviewed journals, KM/IC book authors utilize other sources, including personal research, experts' opinions, personal experience, practitioner magazines, conferences, books, and informal discussions with academics. The model, which was developed within this study, demonstrates that the book's target audience and author's motivation serve as a pure moderator of the relationship between the available content sources and actual book content. Books targeted to practitioners and inspired by a desire to bring theory to practice are based on the author's personal experience and contain many non‐peer reviewed sources, whereas books written for academic readers have content that is mostly derived from peer‐reviewed journals, books, and the author's personal research. Copyright © 2011 John Wiley & Sons, Ltd.
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 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.027 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".