Bibliographic record
Abstract
In the first quarter of the 20th century, luminous neon signs paved the way for the multiscreen aesthetics now punctuating major intersections in metropolises around the world. And yet, these epicenters of spectacle currently bear little or no neon themselves. This article draws from visual studies and histories of electricity to chart a unique material history of neon from novelty to norm, to obsolescence. The article begins with neon’s introduction in France in 1910, followed by its travels across the Atlantic in 1923, when novel neon quickly became definitive of a new urban aesthetic. The best illustration of this is 1940s Las Vegas, where neon flourished as a symbol of glamour and modern progress until, less than a decade later, it lost ground to cheaper and more efficient backlit plastic, fluorescent, and eventually, LED lighting systems. By the 1960s, neon was abandoned to inner cities, noire film, and New Wave journalism, and yet, we still refer to the mega-screen spectacles in numerous cities around the world as bearing this same ‘neon aesthetic’. This article charts this visual journey, demonstrating how neon holds a special significance to urban visual cultures that extends beyond survey histories of electricity and basic light and color theories heralded in traditional visual communications courses.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".