Bidding on events: Critical success factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research was undertaken to gain a better understanding of the nature and competitive importance of bidding on events by destination marketing organizations, with emphasis on identifying event selection criteria and critical success factors for winning bids. Data were collected on the goals and nature of the event bidding process from convention and visitor bureaus in Canada. Canadian bureaus were found to be very active in bidding on a diverse range of events, especially meetings, conventions, political events, and sports. Most bureaus encouraged and assisted other local organizations to make bids and themselves concentrated on major events with city-wide economic impacts. Although event selection criteria were frequently not formalized, respondents stressed potential economic impacts, size, media exposure, time of year, available venues, and local involvement. The most important critical success factors for winning bids were strong partners, excellent presentations, and treating each bid as a unique process, but many respondents also felt their destination needed bigger and better facilities and more marketing/bidding resources. To aid in future research and theory-building, a framework is presented to illustrate event bidding as an exchange process between owners and sellers, including antecedent conditions and event selection criteria.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it