Design Theory: Supporting the Discovery of Novel Knowledge in Organizations
Bibliographic record
Abstract
In my dissertation, I examine novel knowledge discovery in the context of organizational learning. Novel knowledge, defined as knowledge that is potentially strategically important to the organization, not currently known to the organization, indirectly relevant and therefore difficult to find, is proposed to be one of three different types of knowledge that organizations seek to discover in their environment. A taxonomy is developed to differentiate three levels of knowledge discovery in the environment, including goals and tools to support these goals. However, tools supporting the discovery of highly novel knowledge are rare compared to tools supporting the other levels of knowledge discovery. Accordingly, a design theory for novel-knowledge-discovery tools is proposed based on organizational learning theories. The results are proposed to demonstrate how novel-knowledge-discovery tools can support organizational learning.
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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 | 0.035 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".