Food Security Assessment: An Exploration of Canadian Offshore Petroleum SEA Practice
Bibliographic record
Abstract
Strategic environmental assessment (SEA) has the potential to play a crucial role in addressing global food insecurity. This paper presents the results of an evaluation of 17 recent Canadian SEAs, conducted for offshore petroleum exploration, exploring the extent of consideration for food security in current SEA practice. Document analysis was used to appraise consideration of eight core food security elements and conformity to procedural and analytical elements recommended for effective food security assessment in regional SEA. Performance variation among the SEAs in was observed. Notable deficiencies include lack of explicit consideration for food security and lack of transparency around public participation, as well as limited characterisations of the socio-political environment. Some encouraging findings, however, suggest that food security can be successfully addressed in regional SEA. In particular, the ‘system analysis’ approach typically employed in SEA in the offshore petroleum exploration industry is well-suited to food security assessment. Certain aspects of food security are already indirectly considered and incorporated in SEA; yet, there is considerable scope for improvement of integrating food security effectively in SEA.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".