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
During the past five years, Kimberly‐Clark (K‐C) has faced a familiar management challenge: How can senior managers bring the rigor and discipline used to make daily operating decisions to the uncertain and risky world of innovation? The challenge was particularly acute at K‐C because the company is well known for its reliance on Return On Invested Capital (ROIC) and Discounted Cash Flow (DCF), both measures that are widely believed to lead to undervaluation of projects with risky upside potential. This article discusses how and why K‐C adopted and now uses the real options approach to project evaluation and management. The authors also share some lessons learned during the adoption process, including how the company adapted the real options framework to its own circumstances and requirements. The K‐C experience shows that successful adoption rests on a number of factors that have less to do with the rigor or precision of quantitative models than with matters of corporate process and organizational design.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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