Persistence of Vision: Memory, Migration & Citizenship - Free Trade or the Faulure of Cross-Culturality?
Bibliographic record
Abstract
In my novel, Flying in Silence, set in both Australia and Canada, my principal character is a French Canadian man torn between landscapes, languages and allegiances. To represent what was for me the central dilemmas of the novel — reconciling memory and migration — I used the metaphor of Persistence of Vision, that process in film through which we physiologically make sense of, or hold together, what should be a blurred, segmented and impartial sequence of frequently unrelated images. *** Persistence of vision is all about the eye, the way it follows a film, remembers an image, holds on to it, until the next one appears to replace it, so that we are never conscious of the stutter of frames — the space between. Image after image flows past us leaving ghostly fingerprints on shellshocked retinas. Our mind races, slower than light, and we see through the past into the present, just as that present no longer exists. And so we imagine the future. With the old projectors, a glitch could shake that sequence free. Suddenly, we might glimpse a momentary stutter that we’d suppressed — a mother, torn and fractured by a creeping darkness, a loved one felled by another’s lifelong expectations, violence inflicted on a child so that he turns himself inward and disappears.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".