James Lovelock, Gaia Theory, and the Rejection of Fact/Value Dualism
Bibliographic record
Abstract
In this paper the relationship betwwen Gaia theory and fact/value dualism must be understood from two angles: I shall use Gaia theory as a case study to show the philosophical limits of dualism, and I shall also use the discussion of fact/value dualism to clarify the contents of Gaia theory. My basic thesis is that Lovelock is right when rejecting the suggestion that he should clear his theory of evaluative considerations. He is right because in his theory facts and moral values are strictly interwoven and therefore cannot be conceptually separated. I shall show this point by arguing that if we drop the evaluative components from Gaia theory we would not have the same theory cleared of those evaluative components. Instead we would have a theory with a different empirical meaning and different explanatory characteristics.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".