Decision-making processes in biotech commercialization: Constraints to effectuation
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
This research explores two entrepreneurial decision-making processes utilizing effectuation and causation modes in the context of new venture creation in the biotechnology industry. Using a case study approach, we investigate the evolution of three biotech ventures from the start of the venture, featuring major decisions over a period of 10 to 20 years. Assessment of qualitative interviews with founders and CEOs demonstrates that, initially, each company began in effectuation mode and, over time, transitioned to a spectrum between effectuation and causation. The two ventures which retained effectuation logic did not engage in clinical trials. Decision making processes in this study illustrate the interplay between entrepreneurs' ability to manage technological and market uncertainty and circumstantial changes arising from change leadership, venture capital funding and development of lead candidates in the clinical stage of product development.
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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