Bibliographic record
Abstract
Many human preferences, choices, emotions, and actions occur in universally similar manners because they are rooted in our common biological heritage. As such, irrespective of whether individuals are Peruvian, French, or Togolese, they are likely to share commonalities as a result of their shared Darwinian histories. In the current article, I provide a brief overview of how the Internet is a powerful tool for investigating such human universals. Given my work at the nexus of evolutionary theory and consumption, I begin with an example from marketing. Few marketing scholars are versed in evolutionary theory and related biological formalisms (Saad, 2007a; Saad, 2008a). As such, they generally view the environment as the key driver in shaping consumption patterns. This is part and parcel of the blank slate view of the human mind (Pinker, 2002), which purports that humans are born with empty minds that are subsequently filled via a wide range of socialization forces (e.g., parents, advertising content, or movies). Given that marketing scholars rely heavily on the expansive shoulders of socialization in explaining consumption, they are strong proponents of cultural relativism namely the notion that cultures need to be investigated from an emic perspective. Hence, marketers spend much of their efforts cataloging endless crosscultural differences, seldom recognizing that there are numerous commonalities shared by consumers around the world.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".