Bibliographic record
Abstract
News and media have infiltrated society in a variety of ways, and political correctness has been instilled into everyone's mind, but does this really bring justice and freedom to people of color or marginalized groups? In fact, implicit targeting and discrimination continue to pervade modern society, because some reports reinforce and imply that injustice is justified and the unscrupulous media always try to confuse the public in the grey area and provoke more disputes in order to seek profit. Most people may have noticed that people of colour and marginalized groups are not really treated fairly, but the voices of most people are not as powerful as those of influential people, such as artists. For example, Beyoncé shows us how she uses art media (music) to export her power and her thoughts with "Formation", which can be extended into three new concepts: the stereotype of Black people on social platforms; the real influence of Black artists; Black women face far more negativity than Black men. It must be emphasized that art always comes from life, and people need to learn how to see the cruel truth through art, and "Formation" just proves this.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.075 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".