Bibliographic record
Abstract
In a new and incredibly wide-ranging and ambitious work, Kim Sterelny sets out to establish a framework outlining the evolution of our cooperative and social learning capacities (amongst other things).The Evolved Apprentice, the sequel to Sterelny's (2003) earlier Thought in a Hostile World, is without doubt a hefty interdisciplinary achievement of synthesis.Although he approaches the present subject matter as a philosopher, Sterelny states at the outset that his present work is not so much philosophy of science as it is an extended essay in the philosophy of nature: a synthetic argument that is highly empirical, the goal of which is to "develop a plausible first-approximation model of a striking natural phenomenon: the evolution of the distinctive features of human cognitive and human social life" (Sterelny, 2012, p. xi). Creatures of FeedbackA central element of Sterelny's overarching argument is that, rather than posit a single explanatory variable to explain human uniqueness, we should instead aim to provide a co-evolutionary account.In Sterelny's view, human uniqueness gradually emerged from positive feedback loops involving a number of factors.Such factors, according to Sterelny's co-evolutionary account, include cognitive aspects of the human endowment, such as our mindreading and learning adaptations, as well as more broadly (externalist) environmental aspects, such as technological artifacts and apprentice-based learning.At the heart of this picture is a rejection of "magic moment" or "key innovation" scenarios purporting to explain human cognitive and behavioral uniqueness and modernity-such as the harnessing of fire for the first time or the relatively sudden appearance of a new cognitive adaptation.Rather than posit a unitary innovation or some other watershed evolutionary event to bear the explanatory brunt, the co-evolutionary, positive feedback model set forth by Sterelny opens the way for a more gradual path to human uniqueness. Explaining human uniqueness
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.010 |
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".