Care to Wager Again? An Appraisal of Paul Ehrlich's Counterbet Offer to Julian Simon, Part 1: Outcomes
Bibliographic record
Abstract
Objective This paper provides the first comprehensive assessment of the outcome of Paul Ehrlich and Stephen Schneider's counteroffer (1995) to economist Julian Simon following Ehrlich's loss in the famous Ehrlich‐Simon wager on economic growth and the price of natural resources (1980‐1990). Methods Literature review, data gathering and critical assessment of the indicators and proxies suggested or implied by Ehrlich and Schneider. Critical assessment of Simon's reasons for rejecting the bet. Data gathering for his alternative indicators. Results For indicators that can be measured satisfactorily, the balance of the outcomes favors the Ehrlich‐Schneider claims for the initial ten‐year period. Extending the timeline and accounting for the measurement limitations or dubious relevance of many of their indicators, however, shifts the balance of the evidence towards Simon's perspective. Conclusion Although the outcomes favour the Ehrlich‐Schneider claims for the initial ten‐year period, Ehrlich and Schneider.s indicators yielded mixed results in the long run. Simon's preferred indicators of direct human welfare would yield largely favourable outcomes if the bet were extended into the present. Based on this, we claim that Simon's optimistic perspective was once again largely validated.
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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 | 0.088 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".