2018 National Big Data Challenge for High School Students: Think Global Act Local with Big Data
Bibliographic record
Abstract
The goal of this paper is to determine whether there is a correlation between awareness of global warming, and where global warming occurs. This theory is carried out by analyzing maps containing various forms of data that have to do with global warming, such as precipitation and surface temperature, and comparing it with a map of engagement from tweets which mention global warming. This paper found that there is no solid correlation between mentioning global warming in tweets and global warming's effect, although there was a steady increase in both. This is most likely due to Twitter's user base increasing over the years. Therefore it appears that although the effects of global warming have increased, the percentage of people aware of it on major social media sites has not. This then concludes that before trying to find a solution or preventative measure to global warming, an approach must first be made to create awareness for it on social media platforms.
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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 | 0.022 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.017 | 0.031 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".