Bibliographic record
Abstract
The expanding field of network studies, which comprises histories, traditions and innovative research from myriad disciplines such as mathematics, the social sciences, linguistics, computer science, physics, biology, Internet and communication studies may find meaningful dialogue with the field of translation studies. This introductory article seeks to present a multifaceted and multi-tiered historical trajectory of the term and concept “network”, reflecting on the impact it has already had on studies in the domain of the sociology of translation. Can a network-based vocabulary emerging from network theories and studies, including recent works on network society, offer translation studies new conceptual tools with which to think through and articulate translation phenomena? By the same token, how might translation studies, viewing interlingual transfer in terms of product, process, profession, industry, politics and strategy, contribute to the growing body of research on the transmission and exchange of thoughts, ideas, messages, information, values, which characterize communication, the core of all translation activity? As connectivity and connectedness take on ever-important social organizing dimensions in a globalizing multilingual world, a translation-informed network approach as well as a network-informed translation theory approach may symbiotically help us better understanding human and social practices.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.439 | 0.264 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".