Knowledge Transfer for Sustainable Innovation: A Model for Academic-Industry Interaction to Improve Resource Efficiency within SME Manufacturers
Bibliographic record
Abstract
Environmental threats associated with demographic and technological trends have resulted in calls for transition to a global economy that operates within the carrying capacity of the natural environment. Because of their centrality to economic activity, this transition must include small and medium-sized enterprises (SMEs). At the same time, because of their role as knowledge holders on both sustainability and business, higher education institutions (HEIs) can play a more active role in supporting SMEs to address this transition through the provision of timely and appropriate information. Dalhousie University's Eco-Efficiency Centre (EEC) works with SMEs to support them to identify opportunities to pursue sustainability through improved resource (material and energy) efficiency. To date, much of the support for improved resource efficiency within business has focused large corporations; it has not addressed the particular characteristics of SMEs. Supporting that transition needs a different approach, one that understands SMEs' learning dynamics; i.e. their drivers and motivators to apply new knowledge as part of their internal strategies. This paper will discuss one approach taken that focused specifically on developing the absorptive capacity of SMEs to incorporate innovationwhere in this case 'innovation' reflects the strategies for improved resource efficiency. By investigating the relationships and impacts of the EECs involvement with 70 SME manufacturers through their Eco-Efficiency Program for Manufacturers this paper looks at the development of a localized 'knowledge creation and transfer system'. By acting as an interlocutor within this system, they successfully promoted the transfer and integration of resource efficiency knowledge within the sector.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".