“Who Killed the Electric Car” Director Speaks at Lawrence University as Part of Earth Day Celebration
Bibliographic record
Abstract
Environmental filmmaker Chris Paine, director of the thought-provoking 2006 documentary “Who Killed the Electric Car?,” examines the politics, personalities and cold hard cash involved in the return of the electric car in an address at Lawrence University. As part of the college’s Earth Day celebration, Paine presents “How Many Light Bulbs Does it Take to Plug in an Electric Car?” Tuesday, April 26 at 8 p.m. in the Lawrence Memorial Chapel. Paine, who tours nationally to speak on behalf of sustainable transportation, will discuss the challenges of electric vehicles to the car industry and the reasons behind their re-emergence. His latest film, “Revenge of the Electric Car,” is scheduled to make its world premiere on Earth Day (April 22) at the Tribeca Film Festival in New York. In “Revenge of the Electric Car,” Paine interviews the CEOs of Renault-Nissan, maker of the electric Leaf, Tesla Motors, which makes a high-performance electric vehicle and former GM vice chairman Robert Lutz, who has become an advocate for the company’s new Chevy Volt. Paine, who lives in Los Angeles and drives a Telsa Roadster, sees electric vehicles as “something that is fundamentally similar to an iPhone or iPod.” In addition to his advocacy for electric cars, Paine has been active in campaigns to stop deforestation, nuclear testing in Nevada and freeway expansions in California.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.064 | 0.012 |
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".