A wealth of failures: sensemaking in a pharmaceutical R&D pipeline
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
Shadow options are latent opportunities for investment. Current literature attributes the recognition of shadow options to an option chain – initial investments and experiences that provide opportunities to make follow on investments. This article argues, in contrast, that complementary processes of organisational sensemaking and retrospective analysis of project selection criteria enable R&D firms to increase their ability to recognise shadow options. We propose that R&D pipelines that routinely deal with failure (such as drug development) are more apt to identify new investment opportunities because they enable knowledge acquired in the course of project failures to be rapidly incorporated into future searches for shadow options. Future research should investigate the proposition that the success rate in R&D decreases with experience, since greater experience in dealing with project failures motivates the firm to undertake more financially rewarding but riskier R&D.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 it