Upon My Going into a Coffee-House Yesterday, and Lending an Ear to the Next Table. A Corpus-Based Exploration of Coffee House Dialogues and Their Discursive Practices in Late 17th and Early 18th Century England (1662–1712)
Bibliographic record
Abstract
This research presents a corpus-based study which examines various speech-related written genres from the period 1662–1712. The collected texts, comprising transcribed coffeehouse dialogues, plays, poems, and trial proceedings, reflects the popularity of public coffeehouses in England, renowned as social spaces where people gathered news and debated ideas on politics, religion, science, literature, travel and other matters. Although coffeehouses have been studied from the historical, social theorist point of view (Habermas, 1989), this research adds linguistic insight into the experience of these public spaces. Hence, the aim of the study is to explore linguistic features relevant to the socio-historical and pragmatic aspects of the texts, focusing on speech acts, politeness strategies, conversation principles, hedging and assertive utterances. Data retrieved so far reveal a significant tension in the supposed discursive practices. The idea of the coffeehouse as a peaceful place of rational and reasoned argumentation contrasts sharply with linguistic evidence which shows coffeehouse conversations as not always so civil.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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; 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".