Bibliographic record
Abstract
An essential review of the history, benefits, limitations, failures, and politics of pipelines, with a core focus on potential harms to environmental and human health. The United States holds the world record of having the largest network of energy pipelines, with more than 2.4 million miles of pipeline transporting oil or natural gas. Russia, China, and Canada as well as many other countries also have extensive pipelines. How safe is this means of transport, and is there a potential harm to the environment and human health? In this text, professor Madelon L. Finkel presents an essential and clearly-stated review of the pros and cons of transporting oil and natural gas by pipeline. Finkel dispels myths, inaccuracies, and misconceptions and highlights the potential dangers that must be considered in any country's energy policy. Pipeline Politics: Assessing the Benefits and Harms of Energy Policy provides a broad and accessible analysis of pipelines, from their history and safety to their politics and risks. Finkel examines the benefits and costs of pipelines in parallel as well as issues of environmental justice; the fairness of treatment of the people affected; and the development, implementation, and enforcement of pipeline laws, regulations, and policies.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.119 | 0.053 |
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".