Managing Trust: Translating and the Network Economy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In order to understand recent developments in the field of professional translation, we focus in this article on the contemporary network-based translation industry using Albert-Lázsló Barabási’s model of real-world networks and combining it with sociological studies of social capital and trust. According to Barabási, networks are scale-free and therefore fundamentally undemocratic. Barabási’s findings can be used not only by researchers in explaining the topology and organizing principles of production networks but also by professional translators as a conceptual tool in making sense of their current working environment. We use empirical evidence from interviews with six Finnish translators, relating what we discover to be the roles of trust, loyalty, and social capital in networks. The findings suggest that (a lack of) trust may be the Achilles’ heel of these economic networks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it