Early Planning, Collaboration and the Role of Social Media: A Model for Future Event Success and Lessons Learned from Eclipse 2024
Bibliographic record
Abstract
A total solar eclipse is a natural astrological phenomenon that is a special event in the infrequent interludes when it occurs. Two recent total solar eclipses in the United States occurred on August 21st, 2017, and April 8th, 2024. As was initially learned in 2017, such events are best experienced and handled if deliberate and detailed planning takes place before they occur. This paper examines the process that many cities, towns, brands, and companies across the country went through to prepare for and better handle the expected massive influx of interested observers and the important lessons learned that may have significant implications for local communities as well as business practices related to product development and promotion. The total solar eclipse on April 8th, 2024, showcased the potential for organizations and brands to create impactful campaigns that educate, engage, and drive brand awareness while promoting scientific inquiry and a sense of community.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".