Random Digit Darling: The Telephone Turn in the American Social and Behavioral Sciences
Bibliographic record
Abstract
This experimental essay traces the rise of phone-based data collection within the American social and behavioral sciences over the last third of the twentieth century. Exploring aural cultures of surveillance and the eroticization of abstract data, the essay aims to disrupt the privileged status granted to optics within the field of Surveillance Studies-- particularly when it comes to questions of sex and sexualization. Written in the style of the scripts commonly used when conducting phone-based research, the essay positions you (the reader) as a staff interviewer collecting data for a fictional present-day study regarding modern sex practices. Today, the focus of your study is the curious case of 'databaters': a term used for the male masturbators who, as a means to elicit intimate information from unsuspecting call recipients, pretend to be social and behavioral scientists conducting exactly this sort of phone-based research. By the mid-1970s, databaters became one of the most common types of problem calls reported by American women. While the background information provided within your script might also reveal a persistent tradition of intimate transgressions within 'real' phone-based social and behavioral science, any such effect is entirely inadvertent.
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 | 0.016 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".