Bibliographic record
Abstract
It would seem only fair that an author who places a question in the title of a book should be expected to answer it directly. So who does rule the earth? The answer is Napoleon. He did, after all, leave behind a legal code that, as we saw in chapter 1, continues to guide the behavior of billions of people throughout the world today. And then there’s June Irwin, the country doctor who convinced the town of Hudson, Canada, to create rules banning nonessential pesticides and spawned a nationwide movement for change. And let’s not forget her foes in the pesticide industry, who raced across America passing state preemption rules that deny local communities the right to regulate these poisons. The earth is ruled by the Roman Emperor Justinian, who created the legal precedent for public access to beaches (discussed in chapter 2), as well as the real estate developer David Gottfried (chapter 3), co-inventor of the rulemaking system for green buildings known as LEED. The list most certainly includes Edmund Muskie and Philip Hart, the senators who spearheaded passage of the US Clean Air Act and Clean Water Act in the early 1970s. But it also includes José Delfín Duarte, whose local water association is empowered to decide how water resources are managed in his small corner of Costa Rica—an effort that required revising rules at local and national levels. Whether famous figures like Jean Monnet, founder of the European Union, or tenacious groups of citizens like Portland’s Bicycle Transportation Alliance, whether working at the level of empires or that of neighborhoods, the people who rule the earth are those who leave behind a legacy of rules that shape the actions and opportunities of generations to come. If we so choose—if we can put aside for a moment the “little things” we do for the earth, and think about the larger, lasting changes that result when people come together and rewrite the rules they live by—then the group of rulemakers also includes you and me.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.296 | 0.188 |
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".