Translation and Popularization: Medical Research in the Communicative Continuum
Bibliographic record
Abstract
Far from being restricted to exchanges between experts, specialised knowledge is mediated to audiences with different levels of specialization, from scientific reviews to newspaper articles. This diversity constitutes an often-overlooked challenge for translators. As a matter of fact, while documentation and terminology are always crucial, translation decisions are based on communicative parameters as well as cognitive and linguistic criteria. Although it is self-evident that linguistic choices are determined by the proficiency level of the readership, few authors have attempted to specify what those choices are and how the correlation operates, most notably in popularization discourse, and none of them has considered potential differences between languages and cultural settings. The focus of the paper is a bilingual (French and Spanish) corpus study carried out on newspaper articles dealing with stem cell research and cloning published in four different geographic regions (France, Quebec, Spain, Argentina). An original methodology was implemented for data collection and analysis. The number and nature of expressions used to convey each concept were then analyzed. Discursive strategies widely assumed to be a hallmark of popularization, like definitions and explanations, were also taken into account. Indices of metaphorical conceptualization and the underlying modes of conceptualization were identified. This study provides concrete data to a debate that remains largely theoretical, and supports the conception of specialized communication as a continuum. The results go against well-established ideas about popularized texts, specially regarding the trademark status of “didactic features.” It seems imperative to acknowledge the heterogeneity of popularization and to consider the role of textual genre constraints in the way specialized knowledge is introduced. Furthermore, the data obtained seems to substantiate the recent questioning of the canonical view of popularization as a mere 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.
How this classification was reachedexpand
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 | 0.006 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".