Bibliographic record
Abstract
A toddler, not quite two years old, records a 7’ 46” iPhone video. This “perfect” film (after Jørgen Leth’s “perfect” human) exposes the basic operations of smartphone thinking-feeling (Massumi). Drawing on haptic theories of filmic engagement (Marks, Sobchack), I examine how Nova’s camera returns, again and again, to rugs, throws, windows, chairs, and to the hardwood floor. Adult voices—mother, father, grandmother—provide a mundane soundtrack which offsets Nova’s systematic inquiry into medial visual perception. At the film’s center, a brief, surprisingly steady shot gathers the adults at the kitchen counter into a domestic “primal scene” that allows the child to come to terms with both closeness and distance: rather than simply tethering (Turkle) Nova to her parents, the phone works like a small “holding” device, in a Winnicottian sense. Finally, as Nova’s grandmother directs her to “take a picture of my flowers,” and before the phone is wrested from her, it becomes evident that Nova has developed a true method for seeing-being in the world.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".