“Actor-Network Theory” and International Relationality: Lost (and Found) in Translation
Bibliographic record
Abstract
Like any multiplicity, “actor-network theory” is many things: an influential current within the sociology of science and technology; a relational and anti-essentialist form of materialism; an insistence that notions of agency not be confined to human subjects but embrace objects, devices, and other non-human entities; and much else besides. Actor-network theory was initially developed as a way of making sense of the social life of the laboratory and the complex paths that scientific knowledge takes from untidy practice to incontestable “fact.” Its founders, including Michel Callon, Bruno Latour and their collaborators, have since sought to apply these initial insights to a wide range of other arenas of social and political life. In the process, actor-network theory (ANT) has given us a wealth of concepts. The idea of the actor network itself embodies a productive tension, putting structure and agency into an intimate relationship in which the network is made up of actors who are, in turn, the effects of the network.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".