Bibliographic record
Abstract
Closed captions are a vital tool of sonic access for D/deaf and hard of hearing audio-viewers, detailing dialogue alongside notable sound effects and music. As evidenced by the recent virality of the captions in the Netflix series Stranger Things, captions are increasingly playing a key role in the sonic experience for many audio-viewers. From captions such as [tentacles undulating moistly] to [wet footsteps squelch], captions shape and articulate sounds, working both alone and alongside other sonic elements. Yet, while captions crucially anchor sonic meaning for a growing audience, captions are still a critically understudied dimension of film and media sound. Drawing upon the visceral captions and squelching sound effects of the fourth season of Stranger Things, this article details the parallels between closed captions and the custom synchronized sound effects of Foley. Captions crucially emphasize the narrative and characterizing effects of Foley sounds, from an oozing moist [squelch] that turns the stomach to the vivid snap of [bones cracking]. In turn, Foley sound offers a vital new framework from which to understand the sonicity of captions. As an artistic practice of reconfiguration and substitutions, Michel Chion’s seminal distinction between real and rendered sounds underpins theorizations of Foley, where a broken celery vividly renders the emotive impact of bones breaking. This article contends that captions can similarly be understood as rendering sound, a move that ultimately folds captions such as [wet writhing], [creatures chittering] and [flesh tearing] into larger sound theories, highlighting the sonic significance and generative possibilities of access tools.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".