Bibliographic record
Abstract
Abstract from First Paragraph: This essay exclusively examines Holiday’s ebook Hollywood Forever. The e-book is non-traditional and includes a variety of media and performative elements that might otherwise be experienced in art exhibits or installations. The e-book is uniquely crafted to display a blend of visual and auditory features like posters, news reports, and podcasts. Moreover, the multimedia production adds layers of meaning, complexity, and emotion to the text. Holiday’s inclusion of historical materials from American Black culture is a recreation of the Black diaspora archives. Through the unity of old and new media, Holiday weaves together a complex narrative that combines past historical oppression, racial injustice, and intergenerational trauma to recontextualized contemporary social issues. The e-book embodies afropresentism, the combination of digital archival materials, to empower the Black voice. By reshaping history to create space for Black identities, digital texts can participate in the making of their own social and archival construction. The process of rememory uses archival material to reconstruct the narrative of a previously marginalized group. Holiday’s text uses rememory to investigate cultural biases and rearticulate the reader’s approach to racial injustices.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".