Bibliographic record
Abstract
Providing a comprehensive, in-depth analysis of the international conference industry, Conferences and Conventions: a global industry second edition examines the industry's origins, structure, economics, career opportunities, and future development. It also explains its links with the wider tourism industry. Now in its second edition, it is packed with a wealth of new international case studies covering the city of Melbourne, Queen Elizabeth II conference centre, London, Abu Dhabi, MCI Group, the Scottish Exhibition and Conference Centre, Glasgow and team San Jose, California. It also has new sections on:* Market segmentation and web marketing* Conference and event budgeting* Technology and communications, from video conferencing to web casting and pod casting* Corporate social responsibility and sustainable and green events. Conferences and Conventions: a global industry is illustrated with case studies and examples from around the world, including Great Britain, Canada, Australasia, Dubai, Greece, Thailand, South Africa, USA, Austria and many other destinations. It also provides challenging and reflective questions at the end of each chapter so that readers can test their knowledge and think about the issues raised, accompanied by practical assignments.Tony Rogers is Executive Director of the British Association of Conference Destinations and Association of British Professional Conference Organisers, UK
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.100 | 0.049 |
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".