Knowledge Transfer for Sustainable Innovation: A Model for Academic-Industry Interaction to Improve Resource Efficiency within SME Manufacturers
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
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.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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