Mutual Aid: The Other Law of the Jungle. Gauthier Chapelle and Pablo Servigne. Cambridge, Polity Press. 2022. 310 pp
Bibliographic record
Abstract
In 1902, the anarchist Peter Kropotkin published Mutual Aid in which he promoted a radical perspective on evolution in which cooperation, as well as selfishness, drive the form, diversification and organization of life on earth. Despite initial recognition, Kropotkin's contributions have been largely forgotten, even as modern evolutionary theory has recognized the central role of cooperation. In Mutual Aid: the other law of the jungle, Pablo Servigne and Gauthier Chappelle restore Kropotkin's insights to their rightful place as foundational for our understanding of evolution. They further seek to overturn the pernicious misconception of the 20th century, that nature is only selfish, and highlight how understanding the mechanisms that have evolved to drive cooperation in non-human organisms are also manifest in humans, our society and political institutions. Building forward, they present models of societies that generate, or atrophy, cooperation and consider how our current state will consequently affect our ability to respond to global crises. Through their emphasis on cooperation and its ubiquity in life, Servigne and Chappelle end by broadening their argument to suggest that we question the very nature of the self and reinterpret our existence as a component of a broader cooperative environment. Yet despite the compelling empirical evidence presented, one wonders whether skepticism has been neglected as the authors follow a single cooperative narrative. Does modern evolutionary theory reject the selfish gene in entirety, does selfishness acquiesce to the good of the group within our societies always, and is the centrality of cooperation grounds for the dissolution of the self? While recognizing the exceptional contribution Mutual Aid: the other law of the jungle makes to, it is also necessary to rebalance the vivacity of their cooperative narrative.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 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".