Reversing the Translation Flow: Moving Organizational Practices from Japan to the U.S.
Bibliographic record
Abstract
Abstract Building on the neo‐institutional organizational translation approach and on interlingual translation studies, we undertake an historical case study of the movement of Japanese organizational practices to the USA from the 1970s through the mid‐1990s. Both American and Japanese translators struggled to bring Japanese management models into the USA, reversing the dominant translation flow and bridging wide differences between the sending and receiving contexts. We use the translation ecology approach to look at the interactions over time between translators, translations, and translation processes studied separately in much translation research. Our paper makes two contributions to research on organizational translation. First, it develops more precise and theoretically‐based categorizations of the elements of translation ecology – translators, translations, and translation processes. Second, it challenges the generalizability of the decontextualization/disembedding and recontextualization/re‐embedding processes that are widely accepted as a necessary process in moving management models and practices across contexts.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".