Early Planning, Collaboration and the Role of Social Media: A Model for Future Event Success and Lessons Learned from Eclipse 2024
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
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.
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 | 0.002 | 0.001 |
| 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 | 0.001 |
| 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