Bibliographic record
Abstract
This article investigates society’s increasing obsession with transparency through the medium of photography and WikiLeaks. It suggests that Julian Assange’s fixation on exposure as a means to reveal the truth about government systems is reflective of the processes of an ideology of publicity that also works in a society governed through the processes of the spectacle. In this investigation, Retort’s work, claiming that the spectacle hides the violence inherent in neoliberal militarism, is employed to support the ways in which WikiLeaks has become a spectacle in and of itself through its implication in processes of capitalism and the Wars in Iraq and Afghanistan. The paper also investigates the ways in which the increased desire for transparency has accorded itself with the right to visibility, which is ultimately linked to a desire for truth. Judith Butler’s theory of framing is explored to highlight the ways in which information cannot always be entirely contained by the frame (of reality and photography). Ariella Azoulay’s The Civil Contract of Photography also becomes a departure point for elucidating the problematic tendency for those who attempt to reveal what is hidden to become too invested in the potentiality for truth in what is intentionally excluded from the field of visibility.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| 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".