Bibliographic record
Abstract
This note highlights the Biolinguistics Network, its creation, and its role as a promising avenue of research in the biological basis of the language faculty.It also provides insights into the type of material discussed by the Network's participants so far, and the questions that will be addressed in upcoming events.The Biolinguistics Network was created in 2007 to foster multidisciplinary research by setting up a dynamic space to address biolinguistic questions, including what are the principles of our knowledge of language, how this knowledge grows, how it is put to use, how it evolved, and which aspects of the machinery are unique to language as opposed to shared with other domains of knowledge.Such questions were discussed in the two conferences that led to the creation of the Biolinguistics Network.The first, Biolinguistic Investigations, took place in Santo Domingo in February, 2007.The second, Biolinguistic Perspectives on Language Evolution and Variation, was held in Venice in June of that same year.These meetings brought together a number of contributors to the field.Selected papers from these two conferences are assembled in The Biolinguistic Enterprise: New Perspectives on the Evolution and Nature of the Human Language Faculty (Di Sciullo & Boeckx, in press).Such events and related publications, including the multi-authored cross-disciplinary piece published in this issue (Di Sciullo et al. 2010), are exemplars of the catalytic role of the Biolinguistics Network in our understanding of the biology 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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.141 | 0.076 |
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".