Entrepreneurship, Innovation, and Diversification During Times of Crisis: Challenges and Opportunities for Newfoundland
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
The current crash in global oil prices has shown the importance of diversifying economies away from dependence on oil extraction and towards higher value services both within the resource industry and in unrelated industries. Innovation both within the oil and gas industry (by entering the global value chain) or within unrelated industries (such as by applying ROV technology for building offshore wind farms) help detach the economy from dependence on a single economic engine and make it more resilient to economic shocks.<br/><br/>The oil and gas industry in Newfoundland has created a strong foundation for economic development; they contributed to a concentration of human and financial capital in the region that can serve as a platform for continued economic development. Entrepreneurs can potentially use this human and financial capital to diversify the economy away from depending on resource prices However, the same forces that help build up these stocks of capital and skills also create barriers to diversification.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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