Being the King Kong of algorithmic culture is a tough job after all
Bibliographic record
Abstract
This article explores the growing importance of algorithms in digital culture and what they could mean for the visibility and interpretation of culture as a whole. Taking Google as a prime example of a company that participates in widespread information overload whilst simultaneously providing some algorithmic answers to it, we show how it exhibits four different regimes of justification: the techno-scientific, economic, political and moral–aesthetic. These efforts to gain legitimacy operate as a network that is both highly performative and adaptive. For instance, Google builds on and translates such justifications in order for its Project Glass to be widely, if not universally, accepted. But there is another influential mode of performativity at work: the mounting criticism of the device. In the 18 months following the public announcement of Glass, we have observed the media phenomenon and passionate debate it has sparked. What Glass represents is being contested on multiple grounds, and this, in turn, indicates that its meanings will likely remain profoundly ambiguous for some time to come.
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.056 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".