Dataset of the linguistic analysis of the Eastern German Crisis Discourse from 1976 to 1986
Bibliographic record
Abstract
The dataset consists of a ZIP file with the speeches contained in the five volumes of the protocol of the party congress of the Socialist Unity Party of Germany (SED) between 1976 and 1986. The texts have been digitized as PDF files and then converted into machine-readable TEXT files using an OCR software. These TEXT files have been parsed with TagAnt (v. 2.0.4 Windows 10 64-bit), an annotation software. According to the data returned by AntConc (v. 4.0.5 Windows 10 64-bit), four corpora have been created: a main corpus with a total of 184,750 tokens and 16,143 types, a 'corpus A' with 70,533 tokens and 11,964 types, a 'corpus B' with 65,757 tokens and 11,967 types, and a 'corpus C' with 48,460 tokens and 8,145 types. The main corpus includes all speeches from the five volumes, while the three additional corpora have been created based on specific criteria or topics. The 'corpus A' and 'corpus B' have similar token counts and types, and likely differ based on a specific subset of speeches or themes. The 'corpus C' is the smallest corpus, with a focus on a specific aspect of the discourse. This dataset is suitable for exploring and analyzing the Eastern German Crisis Discourse from 1976 to 1986, particularly for scholars who may be interested in political and historical analysis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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; both teacher heads agree on what is shown here.
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".