Multiscalar landscapes: transnational corporations, business ethics and industrial ecology
Bibliographic record
Abstract
This article connects Industrial Ecology (IE) to two literatures which the discipline has, to date, ignored. The first is the human geography literature on 'scale'. I argue that IE suffers something of a blind spot when it comes to appreciating the ontology of multiscalar landscapes. Following the geographers I argue for re-thinking scale as comprising multiple, overlapping socio-political constructions. The second literature is the equally burgeoning one on business ethics. Combining insights from these sources an attempt is made to discern which scales are involved in the formation of business ethics and 'good practice' environmental behaviour, illustrated here with reference to the transnational cement group, Holcim. I propose an analytical framework which brings scale, ethics, and industrial ecology together and suggest that absence/presence of scalar alignment may explain why many evaluations of local industrial symbiosis projects find in practice that success is elusive, patchy and difficult to sustain, but that on the contrary in some cases positive outcomes give grounds for optimism.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".