Entrepreneurship as a driver for advancing the United Nations Sustainable Development Goals: A study in Fiji
Bibliographic record
Abstract
This conference paper argues that entrepreneurs can help society attain UN Sustainable Development Goals (SDGs). We investigate entrepreneurial initiatives at the micro-level with a view on macro- and meso level processes of the SDGs. Our focus is Fiji -- the first country to ratify the Paris Climate Change Agreement in 2016. A new Fiji Water Act had already replaced the Water Supply Act of 1950 and a new Fiji Water Authority established. More recent legal developments have been: (1) the drafting of new legislation to establish the Fiji Water Authority which will supply water to all towns in the country; and (2) draft amendments to the Minerals Act, which (a) establish a requirement to obtain a permit to extract groundwater (and to install bores and wells) within declared areas, and (b) limit polluting activities in declared areas, for the purpose of protecting the quality of groundwater. It is expected that entrepreneurship shall be combined with other societal actors towards the 17 SDGs for the 2030 proposed by the United Nations in substitution to the Millennium Goals (United Nations, 2015). Yet, influence of the institutional environment and how this might foster or diffuse the efforts of entrepreneurship to advance SDGs has so far received little attention. Furthermore, little is known about what policy directions might be suitable to further SDGs at a local level.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".