Bibliographic record
Abstract
The psychological literature today is awash in ungrounded concepts and methods. Although our more sophisticated colleagues are careful to operationalize their concepts (e.g., fear), others use the same concepts with reckless abandon, constructing conceptual edifices on the weakest of foundations. For such “theorists, ” it sometimes seems that evidence has become an inconvenience. One can almost hear them exclaiming: “Evidence be damned. We have minds to explore!” Given this current intellectual climate, it is not surprising that anthropomorphism is popular once again. Along with its fellow travelers—mentalism, introspection, and anecdotalism—anthropomorphism has infected the animal behavior literature in the same way that nativism has infected developmental psychology (Blumberg, 2005). I admire Clive Wynne for his stubborn passion in this struggle. But as I read his astute and perceptive essay—and it should be said that I read it as someone who did not need to be convinced—I found myself aching to change the ground rules of the debate. In particular, I believe it is time to begin demanding that some meat be placed on the anthropomorphism bones. To that end, I would like to see the proponents of anthropomorphism answer some basic questions. Can anthropomorphism form the foundation of an empirical science? It has been argued that anthropomorphism aids the behavioral scientist to discover new facts and generate new hypotheses about animal behavior (Burghardt, 1991). This claim should be testable. Accordingly, I would like to see some effort devoted to documenting whether individuals who explicitly engage in anthropomorphism have a track I thank Ed Wasserman for his helpful comments on an earlier draft of this essay. Correspondence concerning this article should be addressed
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 | 0.058 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.005 | 0.071 |
| Scholarly communication | 0.015 | 0.044 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.013 | 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".