Bibliographic record
Abstract
Abstract Prior to the implementation of ELAN ( tla.mpi.nl/tools/tla-tools/elan , Wittenburg et al. 2006), it was common for sociolinguists to use multiple software applications, and consequently multiple formats, along the route from recording participants to conducting statistical analyses of the data. We present a method which allows for transcription, extracting, coding, preparation for statistical analysis, calculation of some basic frequency statistics, and creation of a concordance all within one program. ELAN is well established as a valuable tool for language documentation. ELAN is frequently used for transcription and multi-tier mark-up illustrating levels of linguistic structure as well as translations and glosses. We hope that this crossover introduction will encourage the efficiency of documentary linguists among sociolinguists and increase the interest in documenting variation among documentarians. After providing an overview of ELAN’s utility, we focus on extracting (or marking) and coding tokens of linguistic variables for quantitative analysis in the variationist sociolinguistic framework. This seamless connection between recording, transcript and coding of dependent and independent variables improves consistency, efficiency, utility, reliability and the accountability of our coding to the original recording. We illustrate a range of benefits and include step-by-step instructions accompanied by downloadable sample files and video clips to illustrate each step of the process ( Extending ELAN tutorial files.zip ). We also include instructions on importing existing (legacy) transcripts into ELAN.
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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".