Bibliographic record
Abstract
A walk through the city of Maputo (Mozambique) becomes a poetic visual essay. The film is inspired by two Mozambican poems, from which it borrows the following themes: shoes, time, space, history, humanity, reading the asphalt, seagulls and hibiscus flowers. Scenes from everyday life and series of portraits from strangers I met in the streets, are linked with radio loops and ambient sounds from Maputo (the film was filmed with a Bolex and the sound is non-sync). The film is honest about the intrusive effects of a camera and the curious gaze of a stranger, but gradually this evolves into more relaxed encounters. Strangeness, obstruction and mutual unease are step by step replaced by cross-cultural contact and communication and mutual enchantment. Distance and closeness are both measured in (film) feet: the theme for a possible poem, and the theme for a possible film. Please note that this a file derived of a video copy of the film, hence the film has not the same sharpness and visual impact as when the film is screened on film on a big screen. Ann Arbor International Film Festival (Michigan, U.S.A.) Reel World Film festival (Toronto, Ontario) Festival International de Figuera da Foz (Portugal) Le Festival International du Nouveau Cinéma (Montreal, Quebec)
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 | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.933 | 0.828 |
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".