Bibliographic record
Abstract
Abstract As is true of any scientific discipline, consumer psychology faces some challenges, many of which can be ameliorated via the use of evolutionary psychology. This includes broadening the scope of the research questions tackled as well as their interestingness; increasing the epistemological and theoretical scope of the discipline; reducing the likelihood of succumbing to the WEIRD sampling bias; decreasing methodological fixation; increasing interdisciplinarity; and augmenting the ethos of replications as well as the field's consilience via the building of consilient nomological networks of cumulative evidence. Modern‐day consumers exhibit preferences and behaviors that are vestiges of evolutionary forces that occurred long ago in deep evolutionary time. Marketing academics and practitioners alike, seeking to unlock the mysteries of what makes consumers tick, can only be enriched in recognizing that Homo consumericus is a product of the same evolutionary processes that have shaped all life forms. To deny this reality ensures that marketing knowledge will remain largely decoupled from biology, and in doing so engender at best an incomplete understanding of consumer behavior.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.041 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".