Bibliographic record
Abstract
There are fundamental differences between “teaching innovation” and “enabling people to innovate”. In this paper we study the technical and behavioral challenges for enabling innovation learning. We present the relationships among emotional resilience and technical performance, major sources of cognitive limitations, bias and technical points of failure, based on theoretical constructs and experiments with nearly 1,000 participants of varied gender, technical and psychological diversity. Our experiments are based on a method that has achieved superior results in learning and impact (40 team-based theses raised US$13 million in funding, obtained 13 patents, and launched 19 startups and 11 corporate ventures in 5 years). The method was designed with the objective of building innovation capabilities in a developing country, and has been refined through exploratory and experimental iterations since 2006 at Universidad Adolfo Ibanez (Chile), Deusto Business School (Spain), MIT (US), and in corporate settings across fifteen countries including the US, Spain, the Netherlands and Belgium. The underlying methodology is based on cognitive load theory, emotional response and decision-making under uncertainty, and mobilization of capabilities. It includes a process, methods and tools for addressing the technical challenges of an innovation journey, as well as thinking and making routines for enhancing cognitive and emotional responses that affect technical performance. We have identified theoretical gaps, and explanations for how cognitive and emotional factors affect teams performance on diverse stages and tasks during innovation, and propose a construct for understanding the mediating and synergic roles of cognition and emotions at individual and team levels. The methodology was awarded the 2015 Wharton QS Reimagine Education Award for best innovation in teaching delivery. It is based on understanding innovation as a highly risky and uncertain journey of learning that is driven by discovery, enabled by empathy, and fueled by failure in order to achieve superior impact.
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 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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".