Bibliographic record
Abstract
The article explores the intricate idea of “legacy” within the context of business events, highlighting its importance not just for individual events, but also for the lasting influence of an organization. Drawing insights from a study by Schot et al., (2023), and more recent critical voices (Lancaster, 2023; Latham, 2023) it underscores the importance of aligning associations’ missions with destinations’ marketing strategies. The role of destinations as enablers in the legacy-building process is highlighted, with a call to prioritize legacy discussions within associations. The article further explores Sarawak’s vision of becoming Borneo’s Legacy capital for business events, detailing legacy impact approaches in Copenhagen and Vancouver. Business Events Sarawak’s (BESarawak) commitment to fostering legacies is showcased through the BESLegacy framework, which aligns with Sarawak’s key areas and the UN’s Sustainable Development Goals (UN SDGs). Instruments like the Anak Sarawak Award and the Legacy Ambassador Programme are highlighted as tangible tools for recognizing legacy impacts. The article concludes with case studies of significant conferences and the contributions of individuals emphasizing the broader impacts of business events in Sarawak.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".