Bibliographic record
Abstract
Extract I am probably best known for my book Linguistic Theory in America: The First Quarter Century of Transformational Generative Grammar (LTA), published in 1980, with a second edition appearing six years later (Newmeyer 1980, 1986b). While the reviews and general reaction to the book were largely positive, there were two criticisms that were raised by more than one reader. First, there was a general feeling that I treated the structural linguists who preceded transformational generative grammar (TGG) with little respect and even less understanding. Second, there were objections to the ‘triumphalist’ tone that pervaded the book. A number of reviewers felt that I exaggerated both the intellectual success of the theory (as measured by the number of its adherents worldwide) and its organizational success (as measured by institutional dominance). Over forty years of reflection have led me to conclude that these critics were essentially correct. This book is in part an attempt to set the record straight, as far as these two issues are concerned. But more basically, my goal here is to explore in depth the transition from post-Bloomfieldian structuralism, which dominated the American linguistics scene from the 1930s to the 1950s, to TGG, which had largely eclipsed it by around 1970.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.602 | 0.546 |
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".