Bibliographic record
Abstract
On March 15, 2019 students from all over the globe participated in the youth climate strike, a political protest with the goal of getting governments to be more active on the matters of climate change. This study examines multiple aspects of the youth climate strike by means of Google Images using the hashtag #YouthClimateStrike. For this study, a sample of 755 Google Images were collected. We analyzed the source of the image (using hyperlink), the characteristics of protest participants in the images and messaging by using the posters within the image. These variables were analyzed using a hybrid method of qualitative and quantitative analysis. The images were derived from 396 unique websites that posted images from the youth climate strike. Of these photos, 85% contained teenaged individuals, 91% of the images contained legible text, with the top slogan being “there is no planet B” and 58% of the text presented in these images contained demands. Future research will be conducted this summer with a larger sample size to determine how COVID 19 has turned this strike into an online strike. Additionally, new research questions will be explored such as whether the slogans are attacking or blaming the government, or if the images differed by publication sources. Presented in absentia on April 27, 2020 at Student Research Day at MacEwan University in Edmonton, Alberta. (Conference cancelled) Faculty Mentor: Shelley Boulianne Department: Sociology
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.
How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".