Individual Exploration, Sensemaking, and Innovation: A Design for the Discovery of Novel Information
Bibliographic record
Abstract
ABSTRACT Discovering novel information can result in the generation of potentially valuable new ideas and can therefore be beneficial to organizations interested in innovation. To be useful, novel information must have a particular relationship to existing organizational knowledge. It must be far enough away to qualify as novel, but it must be close enough that it can be understood and exploited. Therefore, a key challenge for novel‐information discovery (NID) is to find concepts that have such relationships to a given starting point or focal concept of interest. Despite the potential benefits, organizations face a number of challenges when discovering novel information on the Web: locating it, understanding its relevance, and making sense of it given the constraints and biases of existing mental models. In this article, we develop an understanding of the challenges of NID and how a tool can support individuals in locating and translating novel information into novel ideas. Using a design science approach, we develop a design theory for NID. A prototype is developed and evaluated. Our findings show that an NID tool performs better than other Web search tools such as Google in terms of the perceived levels of novel information provided and radicalness of the ideas generated.
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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".