Calibration accuracy of a judgmental process that predicts the commercial success of new product ideas
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 We examine the accuracy of forecasts of the commercial potential of new product ideas by experts at an Inventor's Assistance Program (IAP). Each idea is evaluated in terms of 37 attributes or cues, which are subjectively rated and intuitively combined by an IAP expert to arrive at a forecast of the idea's commercialization prospects. Data regarding actual commercialization outcomes for 559 new product ideas were collected to examine the accuracy of the IAP forecasts. The intensive evaluation of each idea conducted by the IAP produces forecasts that accurately rank order the ideas in terms of their probability of commercialization. The focus of the evaluation process on case‐specific evidence that distinguishes one idea from another, however, and the corresponding neglect of aggregate considerations such as the base rate (BR) and predictability of commercialization for new product ideas in general, yields forecasts that are systematically miscalibrated in terms of their correspondence to the actual probability of commercialization. Copyright © 2007 John Wiley & Sons, Ltd.
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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