Review\tof\tThe\tGovernance\tGap:\tExtractive\tindustries,\thuman\trights,\tand\tthe\thome\tstate\t advantage\tBy\tPenelope\tSimons\tand\tAudrey\tMacklin
Bibliographic record
Abstract
The Governance Gap is a long-awaited contribution to the literature, advocating a stronger role for home state governments in the regulation of extractive companies operating abroad. Tis book arises from the experience of the authors as members of the Harker Commission on human security in the Sudan in the late 1990s.3 Written by Penelope Simons4 and Audrey Macklin,5 Te Governance Gap provides a detailed case study of Canadian company Talisman Energy Inc. and its operations in the Sudan between 1998 and 2003—a period during which the Sudan was “in the midst of a violent civil war” and Talisman was operating through a subsidiary as a 25 per cent partner in the Greater Nile Petroleum Operating Company (“GNPOC”).6 Te appointment of the Harker Commission—a fact-fnding mission sent to the Sudan to examine “the alleged link between oil development and human rights violations”7 —was in part a response to the actions of the US government, which had imposed sanctions and trade restrictions against the Sudan and was pressuring the Canadian government to do the same.8 Te Harker Commission concluded that the confict had intensifed due to the operations of GNPOC. Various reasons for this were cited, including the fact that GNPOC had provided the Sudanese government with access to airstrips and roads, thus “increas[ing] the weight of the frepower it [could] bring to bear.”
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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".