Bibliographic record
Abstract
Mariam Jafri Vs. Maryam Jafri (2019), single screen, HD video, 9:11 minutes. \n\nThe starting point for the video Mariam Jafri Vs. Maryam Jafri is an image of my sculpture, Anxiety, that has been turned into a stock photo for licensing on the Getty Images website, without my prior knowledge or permission. Getty Images is the largest stock photo agency in the world. Getty took the photograph at Frieze Art Fair in London in October 2017. The voiceover traces the work’s trajectory from a readymade sculpture for sale at an art fair to a stock photo for licensing online and finally, to a video commissioned by a Kunstahalle, a space meant to guarantee the autonomy of art. The work reflects upon the role of originality, artist labor and copyright in our culture of sampling and remixing. The video’s title references the caption accompanying the photograph which misspells my name. The title also references an earlier work I made in 2012, called Getty Vs. Ghana which examined the unauthorized copyrighting of African independence day photographs by multinational stock photo agencies, including Getty Images and Corbis. Co-commissioned by TAXISPALAIS Kunsthalle Tirol and Contemporary Art Gallery of Vancouver.\n\nVimeo link: https://vimeo.com/spikefilmandvideo/review/313158285/cabe9ab30b
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.555 | 0.294 |
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".