#ClimateStrike 2019.09.19 - 2019.10.06
Bibliographic record
Abstract
The September 2019 climate strikes, also known as the Global Week for Future, were a series of international strikes and protests to demand action be taken to address climate change, which took place from 20–27 September. The strikes' key dates were 20 September, which was three days before the United Nations Climate Summit, and 27 September. The protests took place across 4,500 locations in 150 countries. The event is a part of the school strike for climate movement, inspired by Swedish climate activist Greta Thunberg. The Guardian reported that roughly 6 million people participated in the events, whilst 350.org—a group that organised many of the protests—claim that 7.6 million people participated. https://en.wikipedia.org/wiki/September_2019_climate_strikes 20-27 September 2019, we saw a record 7.6 million people take to the streets and strike for climate action. The biggest climate mobilisation in history. From Jakarta to New York, Karachi to Amman, Berlin to Kampala, Istanbul to Québec, Guadalajara to Asunción, in big cities and small villages, millions of people joined hands and raised their voices in defense of the climate. The Global Climate Strike shows that we have the people power we need to create a just world and end the era of fossil fuels. https://globalclimatestrike.net Global Climate Strike → Sep. 20–27 – #ClimateStrike Promotion Materials https://globalclimatestrike.net/spread-the-word-climate-strike/#hashtag Hashtags: #ClimateStrike Dates: 2019.09.19-2019.10.06 Number of Tweets: 2,766,462 Size (Hydrated): 21 gigabytes
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.882 | 0.887 |
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".