Bibliographic record
Abstract
Abstract Like language policy, translation policy is a complex adaptive phenomenon where multiple agents interact dynamically at diverse levels and affect how principles such as equality, inclusion, and non-discrimination are implemented at concrete, structural levels. While a fair amount of work has been done on top-down and bottom-up translation policy, more research about meso-level translation policy is needed to better understand this topic. This study thus seeks to show how meso-level agency needs to be taken into consideration; in particular, the complex interplays between meso-level agency and structural factors in policymaking and appropriation processes. Taking two Toronto hospitals as examples, this study demonstrates how hospitals can be conceptualised at a meso-level as collective agents. It examines the complexity of their agentive roles and elucidates their exertion of agentive power in order to explore meso complexity, and to assess the subsequent impact on both policy outcomes and people’s lives. To do so, a framework of parameters influencing, if not determining, the effectiveness of public service translation and interpreting provision is proposed. It is then used in combination with an existing tripartite framework of translation policy to develop a more fine-grained and holistic understanding of translation policy in public service settings.
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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".