How to approach translation in a financial news corpus?
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
This article deals with some of the theoretical and methodological problems that arise when working with a bilingual comparable (i.e., non-parallel) journalistic corpus of financial news that is relatively large (9 million words). The corpus under study comprises two sets of texts drawn from Canadian French and English newspapers in the years between the Tech Wreck of 2001 and the financial crisis of 2007−2008. Following Davier (2015) who advocates for a broadened definition of news translation that includes intralingual activity, the authors make a case for the study of intralingual translation, or rewording, which is a fundamental feature of financial news, as journalists work to popularize specialized knowledge for lay audiences. The methodological challenges of surveying interlingual translation in a sizeable corpus of financial news are discussed in relation with the production of news in Canada. A pilot study using the lexical item “subprime” and its French equivalents illustrates how interlingual and intralingual translation can be investigated in a corpus comprising 18,601 news items. The authors explain how they apply a mixed-method approach (Saldanha and O’Brien 2013) that is based on the interaction between qualitative and quantitative analysis in their research on news translation.
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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