FRED-S: The Freiburg Corpus of English Dialects Sampler
Bibliographic record
Abstract
The Freiburg Corpus of English Dialects Sampler (FRED-S) spans a subset of FRED texts, covering 1,011,396 words and about 123 hours of recorded speech. It consists of 121 interviews with 144 dialect speakers from 5 major dialect areas: the Southwest of England, the Southeast of England, the Midlands, the North of England, and the Scottish Lowlands. The interviews were recorded between 1970 and 2000, while the majority was recorded during the 1970s and 1980s. The FRED-S interviews are available in three formats and can be downloaded here: •\tAudio files in mp3 format •\tTranscripts in txt format •\tPart-of-speech tagged transcripts in txt format We have adopted a ‘notice and take down policy’. We will thus remove a recording or transcript if we receive a written request from someone expressing a genuine objection. If you are concerned that you have found data on this website for which you have not given permission to be published in this way, please contact us at fred@anglistik.uni-freiburg.de. Our heartfelt thanks go to the following archives for providing the opportunity to publish interviews on this website: • Cornish Audio and Visual Archive (last accessed 02 August 2016) • Totnes Elizabethan Museum (last accessed 02 August 2016) • Trowbridge Museum (last accessed 02 August 2016) • Somerset Rural Life Museum/ South West Heritage Trust (last accessed 02 August 2016)
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.018 |
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".