Knowledge Sources and Operational Problems: Less Now, More Later
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
Unlike problems requiring new-to-the-world solutions that combine knowledge from multiple sources, operational problems can often be solved by repurposing existing knowledge from other contexts into new-to-the-firm solutions. Firms that seek new-to-the-firm solutions to operational problems face a cost-benefit tradeoff when deciding how many knowledge sources to use. With less need for knowledge recombination than for new-to-the-world solutions, greater knowledge breadth incurs greater screening and implementation costs without concomitant benefits. We study how U.S. manufacturing facilities from 1991 to 2005 improve operational performance by reducing their rate of annual output of toxic chemical waste (i.e., improvements to operational effectiveness). Results show that search involving fewer knowledge sources in a given year is associated with greater improvements in operational performance (greater waste reduction). At the same time, however, using multiple knowledge sources over time helps improve operational performance, suggesting that avoiding satiation from a single source and learning across sources play temporal roles in toxic chemical waste reduction. Overall, the results suggest that the greatest improvements in operational performance arise with a focused search for new-to-the-firm solutions within periods while also exploring multiple sources over time.
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 | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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