How Do Innovators Learn from Others? Examining Help, Feedback and Advice in Creating Novelty
Bibliographic record
Abstract
Innovators face a particular challenge: they are trying to create something novel, and multiple paths could lead to success or failure. To address this uncertainty, innovators often seek more information from outside their immediate team, firm, or network. The literatures of help, feedback and advice should provide some insight into how innovators learn from external sources. This panel will explore two main opportunities: 1) to bring together three literatures of help, feedback and advice, which have evolved separately; 2) to explore how the literatures apply in innovation contexts when there is no “right answer” or known and predictable outcomes. By bringing together four diverse scholars renowned for their research on help, feedback, and advice, as well as entrepreneurship and creativity, this symposium will facilitate learning as to how these three constructs are used and applied, and identify both points of convergence and gaps to foster application in the context of innovation.
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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 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".