Exploring the linkage between investment in manufacturing and environmental technologies
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
The popular business press, government regulators, environmentalists and the public are calling on operations managers to shift away from their traditional emphasis on pollution control toward pollution prevention when improving environmental performance. Yet, any managerial decision about the level and form of investment in these environmental technologies cannot be made in isolation, but instead must be implemented within the context of other manufacturing investments in process technologies and organizational systems. A survey of two Canadian industries – small machine tools and non‐fashion textiles – revealed evidence that environmental technologies have been regarded as ancillary investments; as investment in manufacturing increased, so did the proportion of that investment directed toward environmental technologies. Further, increased investment in advanced process technologies actually shifted investment away from pollution prevention. In contrast, increased investment in quality‐related organizational systems favored concurrent investment in recycling programs, along with pollution prevention and management systems. Thus, increased investment in quality management offered an important route to expand the implementation of pollution prevention.
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 | 0.002 |
| 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