Bibliographic record
Abstract
This thesis compares the legal and institutional frameworks for protecting human rights in the oil and gas industries of Nigeria and Canada. The thesis identifies environmental human rights as the human rights that are most often violated in these settings, hence the discussion of environmental human rights constitutes a major portion of this thesis. This thesis examines the procedural components of environmental human rights with the aim of recommending certain procedures that may help improve the human rights situation in Nigeria’s oil and gas industry. This thesis examines the history of human rights in Canada and Nigeria. It also explains that environmental rights are human rights. It further identifies the major applicable domestic and international laws and the institutions that are relevant to protecting human rights affected by the oil and gas industries of Canada and Nigeria. Although this thesis acknowledges that Canada and Nigeria have administrative and political differences, it nevertheless argues that they share similarities that warrant the comparison. It furthermore argues that this comparison reveals certain largely procedural recommendations that are advanced here for possible implementation in Nigeria. Overall, this thesis identifies certain procedural differences between the countries’ frameworks. It recommends the implementation of some new procedural mechanisms that will improve the level of human rights compliance within the Nigerian oil and gas industry. The key recommendations include: encouraging public participation through the implementation of a participant funding scheme; reducing the incidences of regulatory overlaps; allowing for the independence of the bodies in charge of impact assessments; utilizing technology during assessment processes; instituting periodic review of regulatory laws; prohibiting problematic conduct; and holding corporate officers liable for corporate violations, among others.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.289 | 0.172 |
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".