The Economic Impact of the UK Meeting & Event Industry
Bibliographic record
Abstract
The UK Economic Impact Study (UKEIS), commissioned by the Meeting Professionals International (MPI) Foundation and undertaken by Leeds Metropolitan University, represents a landmark study for the UK meeting industry. It is, to date, the most comprehensive assessment of the economic impact of the industry on the British economy. Although there has been growing recognition that the meeting industry makes a substantial economic contribution, the evidence base to support such a claim has, until now, been fragmented. Several valuable studies have been undertaken in recent years that have incorporated elements of the meeting industry, but their approach to economic modelling and data gathering have varied significantly. The lack of consistency and alignment with international standards has also prevented comparison between the value of the industry in one country and another. More importantly, it has made it difficult for the representatives of the meeting industry to provide evidence of its significance effectively. In order to provide a comprehensive profile of the UK meeting industry and to measure its economic impact robustly, the UKEIS adopted a framework designed by the UN World Tourism Organisation (UNWTO) for measuring the sector’s economic importance.3 This framework has been used for similar studies of the meeting industry in the USA, Canada, Mexico, Australia and Denmark. One of its particular strengths is that it connects with official national accounting systems, specifically Tourism Satellite Accounts.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".