Industrial chairs: a proven collaboration concept and promising perspective for industry and academia to commonly advance science and improve the management of life cycles
Bibliographic record
Abstract
In order for both to succeed, sustainability research and management require a collaborative approach. Academic research and development of methods to assess sustainability strive to provide decision tools that are scientifically solid and sufficiently consider complexity, but which may not always be well adapted to the reality of a decision process. The need to collaborate and increase mutual understanding of the underlying science and the difficulties and questions faced when implementing changes into existing supply chains is widely recognised in the community. The concept of industrial chairs is well established in North America where the LCA chair of CIRAIG in Montreal is a prominent example in the field of LCA and LCM. In Europe this kind of collaborative approach with shared public and private funding is still rare and can essentially only be found in industrial PhD projects focusing on one doctoral student. Yet it is quite a logical approach offering the opportunity for direct exchange, mutual learning and for research to provide a direct support in advancing sustainability management practice to the benefit of the industrial partners and society in a larger sense. An industrial chair offers the possibility to cover a much broader range of interconnected research questions and case studies to test new developments and more directly react to questions coming from managers. The Industrial Chair for Environmental and Social Sustainability Assessment 'ELSA-PACT' - the first European industrial chair on LCA was launched in early 2014 in Montpellier, is hosted by the national research institute Irstea and co-founded by five industrial partners, four academic and research institutions and four public funding agencies. This presentation will explore the mutual benefits of such an approach, especially the motivations and expected benefits for the partner companies and from an industrial perspective, why to engage in such a project. Starting from the sustainability management challenges encountered by the industrial partners the way to the definition of research questions up to new developments will be outlined and discussed. Examples for mutual learning experiences as well as potential needs for adaptation and deviation from known paths on either side will be presented. Industrial chairs have proven to be a successful collaboration and funding concept in North America and certainly are a promising approach in the European context.
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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 | 0.027 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.024 | 0.028 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".