Crisis Text Line Benefit Festival: An Applied Study in Event Planning and Practice
Bibliographic record
Abstract
For my Honors Capstone Project, I created and hosted my own benefit concert as an opportunity to gain real experience in event management, booking, production, marketing, and promotion. Although I’d had incredible experiences in my extracurricular activities and internships, I had not yet been able to conceptualize my own event and see it all the way through. The Crisis Text Line Benefit Festival took place on Wednesday, October 25, 2017, at Funk N’ Waffles in downtown Syracuse. All proceeds were donated to Crisis Text Line, a non-profit organization that provides 24/7 counseling services via text message. The two performers were Laura Stevenson, an indie rock singer from Rosedale, NY and Cat Clyde, a blues singer from Stratford, Ontario. In addition to the live performances, the show was live streamed online for donations, provided custom merchandise, and had two component pages for ancillary fundraising. Money raised from this event helped to save more lives each day by funding counselor training, covering texting fees for thousands of at-risk texters, or expediting the global expansion of Crisis Text Line. My goals for my Honors Capstone Project were to hone these event planning and promoting skills and become a better music industry professional while doing my part for suicide prevention.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".