Data in support of: A’ faighneachd Mhic-Talla [Asking the Echo]: A corpus-based approach to vernacular classification of Gaelic songs
Bibliographic record
Abstract
This dataset is in support of the Master's thesis research entitled "A’ faighneachd Mhic-Talla [Asking the Echo]: A corpus-based approach to vernacular classification of Gaelic songs." The purpose of this research was to identify vernacular classifications of Gaelic songs. The original data for this study consisted of textual data from the all-Gaelic newspaper Mac-Talla (1892-1904) and was sourced from two institutions: DASG (Digital Archive of Scottish Gaelic) and the Angus L. Macdonald Library at St. Francis Xavier University. Two pages that were missing from the Angus L. Macdonald Library collection were not included in this study: October 8, 1892, volume 1, number 20, pp. 3-4. File 01 (README.txt) contains contextual documentation for this dataset. File 02 (CM_KWIC_char20_2025-05-25_v01_raw.tab) is comprised of the raw concordance data generated in R of 20-line character strings from the keywords "òran" (song) and "amhran" (song [dialectical variation]). File 03 (CM_oran_2025-04-30_v01_final.tab) is the final dataset of the adjectival and genitive constructions of "òran" and "amhran." File 04 (CM_category_2025-04-30_v01_final.tab) is the final dataset of the song categories that were manually extracted from the concordance data.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.080 |
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".