Speaking for informal interaction: a handbook for English Department Undergraduate Students Faculty of Letters and Humanities UIN Sunan Ampel Surabaya
Bibliographic record
Abstract
First of all, I’d like to express my deepest gratitude to Allah for His blessings so that I can finish writing this textbook “Speaking for Informal Interactions” as one of the supporting systems in S1 program, in the Department of English Letters at the Faculty of Letters and Humanities of IAIN Sunan Ampel Surabaya. Informal English in its native countries is varied based on its geographical and cultural background. Since people native in English have spread in different regions, even continents, they differ the way of communication in their origin language: English, especially in informal situation. Simply to say, there are four English native countries in the world. They are the United Kingdom (The Great Britain), the United States of America, Australia, and Canada. Even though they root to one language which is English, each has its own style of informal communication. The way an American greet people may be different with a British in informal situation. This book will lead the students to the American one. Therefore, the utterances or expressions exist in the book will probably fit only to the people from the United States, or who experience with American English style. This textbook “Speaking for Informal Interaction” is designed to serve several purposes. The most essential goal is to enable students to express their ideas through popular utterances in informal English, especially in American style. The content of this textbook consists of: Openings and Closings, Introductions and Address Systems, Invitations, Thanking People and Replying to Thanks, Apologizing, Expressing Anger and Resolving Conflict, Giving Compliments and Replying to Compliments, Getting People’s Attention and Interrupting, Agreeing and Disagreeing, Controlling the Conversation, and Getting Information. The book contains the common expressions used by the American people in their daily life. It provides examples of dialogues, certain phrases to use in certain situations, and tasks in order to practice the expressions by the students themselves. Hopefully, this book can help and guide the students to be good speakers of English.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.031 |
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".