Bibliographic record
Abstract
In an article entitled ‘The History of Transhumanism’, published in Notes & Queries in July 2015, Peter Harrison and Joseph Wolyniak1 rightly point out a common error found in standard academic articles and on the Internet—particularly Wikipedia, which cites these academic sources in good faith—according to which the word transhumanism was ‘invented’ by Julian Huxley in 1957 (or even in 1927, certain sources claim). Referring to the use of the term by the Canadian essayist W. D. Lighthall (1857–1954)—who evokes ‘St Paul’s transhumanism’—the authors draw a connection between this religious use of the term (transhumanism as man’s capacity to transcend the human condition in his encounter with God) and Dante’s use of the term in The Divine Comedy (through the Italian verb he coined, trasumanar, which Carey, the English translator of Dante, translated in 1814 as transhuman change). As the authors suggest, tracing the history of the word transhumanism is an important matter given the contemporary upsurge in the use of the term. The fact that the term is practically identical in the Latin and Anglo-Saxon2 languages facilitates this research and makes it all the more useful. Indeed, understanding how the word made its (re)appearance in the evolutionist discourses of the 1930s to the 1960s sheds light on contemporary usage of the term. After correctly noting that Julian Huxley used the term in a lecture of 1951, Harrison and Wolyniak suggest that he may have read the word in Lighthall when he was looking for an alternative to evolutionary humanism to describe his own scientific utopia. This is a plausible hypothesis, but we would like to introduce another to the debate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.014 |
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".