Bibliographic record
Abstract
The conclusion discusses phenomena not addressed elsewhere, such as novels in production at the same time as their film adaptations (as in Christine Leunen’s Caging Skies(2019), adapted by Taika Waititi as Jojo Rabbit (2019)) as well as Indigenous and partly-Indigenous films made from settler-colonial cultural texts, as in Jeff Barnaby, Kent Monkman, and Caroline Monnet’s short films made from pre-existing footage from Canada’s National Film Board about Indigenous people and the Australian film Ten Canoes, derived from ethnographic photographs and co-directed by Rolf De Heer and Peter Djigirr. Although we see some mainstreaming of Indigenous cinema and culture through Waititi’s Academy Award for Best Adapted Screenplay and his land acknowledgment at the Oscar pageant, it remains the case that not all texts or their adaptations circulate in the same way or with the same reach within and across the global cultural marketplace. However, when these stories reach us from across and via the global cultural marketplace, it is our responsibility not just to take pleasure in them, or take them at face value, but also to consider other versions of these stories, who has told them, and for whom.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.139 | 0.044 |
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".