Leaner, cleaner, and full of attitude
Bibliographic record
Abstract
Early fieldwork for the Linguistic Atlas of the United States and Canada (later, theLinguistic Atlas Project, or LAP) consisted of 6- to 8-hour-long interviews designedto elicit lexical, phonological, and grammatical targets from native informants inpredominantly English-speaking communities throughout North America. Fieldworkers for the project were trained to ask questions that were usually in the formof descriptive phrases that tasked the speaker to name the item being described(these were often framed by the phrase what would you call [description]) or fill-in-the-blank questions that asked the speaker to supply the target as the missingword (if a glass fell on a hard floor and shattered, you would say the glass ______).For the earliest surveys, responses were written down in the International Phonetic Alphabet (IPA), but later interviews were tape recorded in their entirety andthe responses transcribed from the recordings. The ultimate goal of the earliest surveys was the mapping of dialect boundaries, particularly the mapping of individuallinguistic features. Over time, however, the goals and structure of the LAP interview have changed.The overarching goal of LAP interviews has shifted from an interest in isoglossesand dialect boundaries to a desire to record variation and to investigate the correlations between that variation and specific social and regional groups. Today’sLAP interviews take the form of conversational interviews that still seek namesfor specific foods, animals, weather phenomena, etc., as well as morphological andsyntactic forms, but also address the contemporary sociolinguistic interest in perceptions and attitudes. This paper provides details of the new hybrid LAP interview,one whose format offers the best of all worlds: a set of dialectological targets thatwill facilitate comparisons across LAP surveys and through time, the free-flowingconversation prized by traditional sociolinguistics for grammatical andphonological analysis, and questions aimed at getting at interviewees’ attitudes and beliefsabout language use in their communities. Interviews have already been conductedin Kentucky with this new framework.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.066 | 0.031 |
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".