Bibliographic record
Abstract
Market value for nascent goods can be unknown for a number of reasons. It can be discovered only through market experience. Does concentrated commercial leadership or dispersed commercial leadership more efficaciously explore value? Concentrated or dispersed commercial leadership describes whether a small or large range of firms, respectively, can commercialize potential services and products for a similar, though uncertain, technological opportunity. Dispersed commercial leadership explores the unknown more quickly than concentrated leadership. Most new firms exploring a technological opportunity do not survive a market test. A wider variety of firms increases the chances that at least one will survive. Beyond selection, more dispersed commercial leadership has another, more subtle effect. It increases the likelihood that a so-called contrarian reaches the marketplace sooner. This effect can be readily visible if the contrarian quickly spurs innovative responses from established firms who otherwise would not have taken any action
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.198 | 0.080 |
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 source (direct Gemma or distilled Codex), 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".