Doing More with Less: Building Dynamic Capabilities for Eco‐Efficiency
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
Abstract This article sheds light on the manner in which managers perceive, develop and integrate dynamic capabilities for eco‐efficient activities inherent to industrial ecology. The research employs a case study of 12 Canadian facilities involved in the processing of a wide variety of waste materials. Findings from the experiences of 60 managers interviewed reveal that capabilities for industrial ecology largely depend upon the integration and coordination of competencies, innovations and new routines related to several functional areas: innovation and technological development; control of residual material flows; adjustments in human resources; management of environmental constraints; and networking and marketing. These dynamic capabilities are developed and integrated through a four‐stage process: local experimentation, internal operationalization, enlargement/cross‐functional integration and strategic consolidation. The paper contributes to the extant literature related to dynamic capabilities and the natural resource‐based view by offering an understanding of those factors necessary for the success of industrial ecology, and also by demonstrating the functional and dynamic nature of such factors. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment
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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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