Bibliographic record
Abstract
Evolution by natural selection applied by sociologists has been met with great resistance since Herbert Spencer (1820-1903), marked by dark notions of power and authority associated with an uncritical and enthusiastic application of natural selection to fashionable notions of race and privilege. E.O. Wilson’s (1929-) Sociobiology (1975) attempted to reignite the possibilities of evolutionary discourse using modern genetics to social systems but was stymied by the racialized legacy and liberal notions of “genetic determinism.” Here, the sociobiological framework is re-imagined by extending it from individual gene mechanisms and behavior, into social figurations of large-scale actor networks. Using the conceptual tools and historical analysis of Norbert Elias’s, The Court Society (1983), detailing Louis XVI’s court and the interdependencies between its members, I will be suggesting that these networks of interdependence composed of individual actors are facilitated and constrained by the processes of natural selection, and therefore can be analyzed as such. The entanglement of dependencies created by actors within a network formulates “massing” points that identify the networks form and function as a “social organism”. The value gained in understanding the organic fluidity of social networks, how they are formed, shaped, evolve, and come into conflict with competing social figurations, may provide a new and naturally derived way of interpreting interdependent social actor networks, and provide greater depth into the conceptualization of human social relations. Finally, such a view of history and sociology would align with the principles of Big History by understanding the human subject as bound to the same processes of development that have been occurring to all forms of matter in the Universe over the last 13.8 billion years.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.047 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".