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
Dow Chemical Company, which was founded in 1894, is now the second‐largest chemical company in the world. From the outset, the company has been committed to high‐technology research and commercial innovation in chemistry, advanced materials, and agro‐sciences. But if Dow's long history of innovation is impressive, the greatest change in the past few years has been the company's use of innovation to reinforce its commitment to sustainability. In 1996, the company produced its first set of 10‐year sustainability‐related goals. In an effort to meet such goals, the company invested a total of $1 billion in environmentally beneficial products such as new seeds and traits in Dow's AgroSciences business, solar shingles, and advanced battery technologies. Along with the social benefit of higher crop yields and reduced carbon emissions, the company's return on this investment has been estimated at $5 billion. The company was even more ambitious when setting its next set of 10‐year goals in 2006. In this statement, Dow's leadership aimed to create a culture that saw sustainability as a business opportunity from the perspective of a “triple bottom line”—a performance evaluation scheme focused on “people, planet, and profit” that construes success in terms of social benefits, environmental stewardship, and economic prosperity. Dow is now starting the process of developing its third set of 10‐year goals, with the aim of producing a plan that will ensure the viability of the company 50 years from now. With this end in mind, Dow's leaders understand their obligation to continue investing in the health and well‐being of their employees, their communities, and the environment while still creating value for their shareholders.
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 | 0.000 |
| 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