Bibliographic record
Abstract
I first met Noam Chomsky through a project that attempted to get the baby chimp Nim Chimpsky to “talk.” At nineteen, with the certainty of youth, I knew that I would soon be “talking to the animals.” Nim was the focus of our Columbia University research team’s Grand Experiment: could we teach human language to other animals through environmental input alone with direct instruction and reinforcement principles? Or would there prove to be aspects of human language that resisted instruction, suggesting that language is a cognitive capacity that is uniquely human and likely under biological control? Nim was affectionately named “Chimpsky” because we were testing some of Chomsky’s nativist views. To do so, we used natural sign language. Chimps cannot literally speak and cannot learn spoken language. But chimps have hands, arms, and faces and thus can, in principle, learn the silent language of Deaf people. By the early 1970s, a surprising number of researchers had turned to learning about human language through the study of non-human apes. Noam Chomsky had stated the challenge: important parts of the grammar of human language are innate and specific to human beings alone. Key among these parts is the specific way that humans arrange words in a sentence (syntax), the ways that humans change the meanings of words by adding and taking away small meaningful parts to word stems (morphology), and the ways that a small set of meaningless sounds are arranged to produce all the words in an entire language (phonology). The human baby, Chomsky argued, is not born a “blank slate” with only the capacity to learn from direct instruction the sentences that its mother reinforces in the child’s environment, as had been one of the prevailing tenets of a famous psychologist of the time, B. F. Skinner.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".