Bibliographic record
Abstract
Harking back to Langdon Winner's now classic essay “Do artifacts have politics?,” my aim in this article is to ask a very similar question—namely, do artifacts have political economy? Following Winner and with the same objective in mind, I analyze artifacts that: (1) have been designed in ways that embed particular political economies; or (2) are compatible with particular political economies. I illustrate the former using Winner's own example of Robert Moses and the design of bridges in New York City. For the latter, I illustrate a strong and weak version of the compatibility claim, with the strong version characterized by the adoption of both a particular technology and political economy while the weak version is characterized by the adaptation of the social context to a particular technology and political economy. I use the example of advertising technology (“adtech”) and generative artificial intelligence respectively to illustrate these two versions. I frame this discussion within an approach I define as constructivist political economy sitting at the interface of science and technology studies and political economy, which can provide a useful analytical tool to analyze and address the vagaries of contemporary technoscientific capitalism.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".