The emergence of a new paradigm in ape language research
Bibliographic record
Abstract
In recent years we have seen a dramatic shift, in several different areas of communication studies, from an information-theoretic to a dynamic systems paradigm. In an information processing system, communication, whether between cells, mammals, apes, or humans, is said to occur when one organism encodes information into a signal that is transmitted to another organism that decodes the signal. In a dynamic system, all of the elements are continuously interacting with and changing in respect to one another, and an aggregate pattern emerges from this mutual co-action. Whereas the information-processing paradigm looks at communication as a linear, binary sequence of events, the dynamic systems paradigm looks at the relation between behaviors and how the whole configuration changes over time. One of the most dramatic examples of the significance of shifting from an information processing to a dynamic systems paradigm can be found in the debate over the interpretation of recent advances in ape language research (ALR). To some extent, many of the early ALR studies reinforced the stereotype that animal communication is functional and stimulus bound, precisely because they were based on an information-processing paradigm that promoted a static model of communicative development. But Savage-Rumbaugh's recent results with bonobos has introduced an entirely new dimension into this debate. Shifting the terms of the discussion from an information-processing to a dynamic systems paradigm not only highlights the striking differences between Savage-Rumbaugh's research and earlier ALR studies, but further, it sheds illuminating light on the factors that underpin the development of communication skills in great apes and humans, and the relationship between communicative development and the development of language.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.058 |
| Scholarly communication | 0.012 | 0.029 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".