Individual Exploration, Sensemaking, and Innovation: A Design for the Discovery of Novel Information
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it