Bibliographic record
Abstract
Purpose The purpose of this paper is to report on research that aims to make knowledge, and in particular know-how, more easily accessible to both academic and industrial communities, as well as to the general public. The paper proposes a novel approach to map out know-how information, so all knowledge stakeholders are able to contribute to the knowledge and expertise accumulation, as well as using that knowledge for research and applying expertise to address problems. Design/methodology/approach This research followed a design science approach in which mapping of the know-how information was done by the research team and then tested with graduate students. During this research, the mapping approach was continuously evaluated and refined, and mapping guidelines and a prototype tool were developed. Findings Following an evaluation with graduate students, it was found that the know-how maps produced were easy to follow, allowed continuous evolution, facilitated easy modification through provided modularity capabilities, further supported reasoning about know-how and overall provided adequate expressiveness. Furthermore, we applied the approach with various domains and found that it was a good fit for its purpose across different knowledge domains. Practical implications This paper argues that mapping out know-how within research and industry communities can further improve resource (knowledge) utilization, reduce the phenomena of “re-inventing the wheel” and further create linkage across communities. Originality/value With the qualities mentioned above, know-how maps can both ease and support the increase of access to expert knowledge to various communities, and thus, promote re-use and expansion of knowledge for various purposes. Having an explicit representation of know-how further encourages innovation, as knowledge from various domains can be mapped, searched and reasoned, and gaps can be identified and filled.
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 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".