Taking Leaps of Faith: Evaluation Criteria and Resource Commitments for Breakthrough Inventions
Bibliographic record
Abstract
Breakthrough inventions form the basis of new technological standards, successful business ventures, and improve the well-being of society. However, predicting whether an invention truly has such potential is extremely difficult, and financially backing such innovations is risky. Because they are not fully formed, organizations and their evaluators must take a leap of faith in supporting cutting- edge invention concepts. We develop arguments for how and why evaluators decide to offer resource commitments to potential breakthrough inventions, despite the red flags raised using standard evaluation criteria. We tested our theory on nearly 700 invention evaluation reports written by a university’s technology transfer officers. Our results based on the interaction of standard evaluation criteria (i.e., feasibility and desirability) and the emotion of awe provide clues for when evaluators take a leap of faith. While backing potential breakthroughs increases the variance in outcomes, they are vital for any organization with a pro-social mission. Using the context of the research laboratory, our study insights can be applied to many management situations in which new ideas are assessed for resource commitments.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".