Bibliographic record
Abstract
This thesis compares the legal and institutional frameworks for protecting human rights in the oil\nand gas industries of Nigeria and Canada. The thesis identifies environmental human rights as\nthe human rights that are most often violated in these settings, hence the discussion of\nenvironmental human rights constitutes a major portion of this thesis. This thesis examines the\nprocedural components of environmental human rights with the aim of recommending certain\nprocedures that may help improve the human rights situation in Nigeria’s oil and gas industry.\nThis thesis examines the history of human rights in Canada and Nigeria. It also explains that\nenvironmental rights are human rights. It further identifies the major applicable domestic and\ninternational laws and the institutions that are relevant to protecting human rights affected by the\noil and gas industries of Canada and Nigeria. Although this thesis acknowledges that Canada and\nNigeria have administrative and political differences, it nevertheless argues that they share\nsimilarities that warrant the comparison. It furthermore argues that this comparison reveals\ncertain largely procedural recommendations that are advanced here for possible implementation\nin Nigeria.\nOverall, this thesis identifies certain procedural differences between the countries’ frameworks.\nIt recommends the implementation of some new procedural mechanisms that will improve the\nlevel of human rights compliance within the Nigerian oil and gas industry. The key\nrecommendations include: encouraging public participation through the implementation of a\nparticipant funding scheme; reducing the incidences of regulatory overlaps; allowing for the\nindependence of the bodies in charge of impact assessments; utilizing technology during\nassessment processes; instituting periodic review of regulatory laws; prohibiting problematic\nconduct; and holding corporate officers liable for corporate violations, among others.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".