Bibliographic record
Abstract
Drawing inspiration from Benjamin’s analysis of the small ‘crystals of the total event,’ and Barthes notion of imagistic punctum, this essay examines an irritating ‘cinematic splinter’ derived from Arrival (Villeneuve US/Canada 2016). The grating key scene witnesses the film’s only significant African-American character, Colonel Weber (Forest Whitaker), remind the white linguistic professor, Louise Banks (Amy Adams), that ‘a more advance race nearly wiped out the [Australian Aborigines].’ Exploring this excruciating spec helps to explode a kaleidoscopic image of our epoch, within which we can perceive a contracted ‘montage of history’—that counterbalances the sf story’s teleological projection of future. Among other things, the Manichean scene foregrounds how—as has historically been the case with Hollywood fare—perceptions of past and future become negotiated through a shifting web of racial and ethnic hierarchies. Recognising this, the essay explores how the scene’s contrived mise-en-scene amplifies Banks/Adams’ otherwise ‘invisible’ white profile, while using Weber/Whitaker to enfold divergent Black histories associated with past colonial contacts. This in turn helps conger images of a more complex and contested global history, or what Deleuze calls a ‘world-memory,’ that forces us to consider the real-world context of the here-and-now, wherein China appears on the ascendance.
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 | 0.009 | 0.017 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".