Bibliographic record
Abstract
The rise of social media has created a new form of activism whereby individuals can connect globally to stand against oppression. Social media has provided a platform for people to share their opinions on important issues and raise awareness. The modern generation has a unique reliance on technology and have found ways to use content as a tool. Social media account holders are able to influence and create action. This virtual globalization allows for new ideas and thoughts to be broadcasted freely and enables individuals to find their respective groups online. It has also created a sense of community and belonging for people who may not have had access to such support in the past. As a society, we are aware of the negative downsides of social media and its effect on the confidence and status of individuals. The case study I will explore in further detail is that of Zhina (Mahsa) Amini, also known as Iran’s Woman, Life, Freedom movement. I will analyze the influence of social media following her death and the geo-political context that explains the current polarization in Iran. Exploring the effects of social media in Iran through the Amini case can provide greater insight into how a community is built and maintained during a tragedy.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".