Honor & respect the official guide to names, titles, and forms of address
Bibliographic record
Abstract
"Honor and Respect is the definitive guide to first impressions. For any personal or professional situation where formality is of the essence and proper decorum is the expectation, this book offers critical information on how to address, introduce, and communicate with officials, functionaries, and dignitaries from all walks of life and over 180 countries. From presidents to pastors, ambassadors to attorneys general to your local alderperson, Honor and Respect offers clear explanations and examples of the official honorifics of thousands of federal, state, and municipal officials; corporate executives; clergy; tribal officials; and members of the armed services in the United States, Australia, Canada, and the United Kingdom. It also includes titles and guidance on addressing high officials from more than 180 countries. Painstakingly researched and carefully vetted, the book's contents are indispensable for individuals or offices working in government, foreign affairs, diplomacy, law, the military, training and consulting, and public relations, among others. This updated third edition reflects the nuanced changes in language, protocol, and conventions that have been implemented by the State Department, Armed Forces, and myriad other government offices in the US and beyond"--
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.203 | 0.182 |
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".