Bibliographic record
Abstract
The self-interest vision may require something like altruism to establish its own distinctiveness, but for the vision itself, altruism is an embarrassment. This is evident in the all out assault on altruism in sociobiology, an assault that extends to the ordinary, as well as the biological, sense of the term. It is also buttressed by attempts to understand ethics without reference to anything like altruism. From various directions attempts have been made to base ethics on self-interest, in the conviction that this is the only serious motivation that people can be expected to respond to today. MOTIVATIONS BEHIND SELF-INTEREST ETHICS The infamous instance of self-interest ethics is the social Darwinism championed by Herbert Spencer. “Survival of the fittest” became not only a summary of the understanding of the processes that made life possible, but also an ethical mandate indicating the inevitable course that would be sustained by the future unfolding of these processes. This biologizing of ethics has been refined and reinforced through the much more precise mechanisms of sociobiology. The most prominent pioneer of this development, E. O. Wilson, proposes that “the time has come for ethics to be removed temporarily from the hands of philosophers and biologized.” He expects that this will produce “a biology of ethics, which will make possible the selection of a more deeply understood and enduring code of moral values.” The basis for such a biology of ethics is sketched by Richard D. Alexander in The Biology of Moral Systems .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".