THE ENDOSCOPIC GAZE: OBJECTIVITY AND OBJECTIFICATION GO INSIDE THE BODY (AND OUT AGAIN)
Bibliographic record
Abstract
Endoscopy is a medical technique used for exploratory or surgical purposes, wherein a small camera-like device attached to the end of a probe is inserted into the body through a natural orifice or an incision if necessary. In the past, starting as early as the beginning of the 19th century, the endoscope was just that, a scope, and the physicians were the only ones who saw what it showed. However, in the past 20 years, a digital camera has been added to the scope, allowing surgeons to operate not only by looking directly into the scope but at a television-like monitor as well. Often the patient is able to view the exploration of his or her own body on this screen or might see a recording after surgery. With this evolution in the technology to include the creation of images or representations of the body, endoscopy’s impact transcended the medical field to become a form of media, a way of representing the world of the body and the body in the world. José Van Dijck points out that further explorations of endoscopic technology increased when the media disseminated video images of the inside of the body, sparking the interest of the general public. This interest only grows as endoscopic technologies continue to advance and their sociocultural impacts continue to develop.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".