David versus Goliath: Harnessing the Power of SMEs in the Fight for Sustainability
Bibliographic record
Abstract
Climate change, resource depletion, environmental and economic disparity are the twenty-first century Goliaths.Governments, NGOs and corporations when "fighting" the Goliaths often overlook small and medium enterprises, the twenty-first century Davids.SMEs have a substantial aggregate impact and are frequently referred to as the "economic engine" of a country.In this conceptual paper, the authors demonstrate that, due to SMEs' aggregate impact and economic functions, their participation in sustainable development is essential.Most SMEs are intimate with their customers, rely heavily on their local economy, and their owner-managers have stronger motivations than mere profit maximization.This provides the incentive for them to participate in the betterment of their communities.While governments, NGOs, and large corporations are increasingly recognizing SMEs' importance, there is frequently a gap between their rhetoric and actions in engaging them.SMEs themselves find the concept of SD ambiguous and the terminology inappropriate to their operations.Those that strive to adopt sustainable practices and develop sustainable initiatives frequently are unclear on the appropriate tools or lack the resources with which to do so.This paper identifies key factors that will enable SMEs to not only become sustainable enterprises, but also to champion SD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".