Bibliographic record
Abstract
[Introduction]: The historical canon of poetry is predominantly male. The historical domain of policy making and politics is predominantly male. In the digital age, however, where the means to share or publish one’s thoughts and views is available to almost anyone, the strict gatekeeping of literature and political discourse is no longer upheld. The phenomenon of instapoetry, poetry published to Instagram, is an example of a social media platform being used by women to bring poetry into popular culture, and, by that means, address political issues surrounding womanhood. By addressing issues of female oppression, sexual assault, and race through poetry, female instapoets wield political power by raising awareness about these issues and influencing and mobilizing their young and female demographic to instigate social change. Rupi Kaur, a famous Canadian-Indian instapoet with 4 million Instagram followers, is an exemplar of the intersection of poetry, social media, and politics. Kaur’s female-centred content reaches millions of people and speaks to healing by way of self-help. Through her words and illustrations, readers are encouraged to think about the politics of being a woman today.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".