Bibliographic record
Abstract
In the context of the growing lack of understanding and even cultural conflict that plague the heterogeneous societies of today, translation naturally has its place in mass communication. Seen as the privileged locus of intercultural communication, translation is not only the expression of the necessity of intercomprehension between differences, but also, and more importantly, the necessity of the determined search for areas of incommunication (Wolton). Hence, taking into account the difficulties and the points of discord as a priority instead of aiming for compromise and pacific appeasement in the citizen-translational operation takes the shape of an emergency. Translating means, first and foremost, translating that which is not going well, that which we understand the least. In the same vein as Jakobson's project, who suggested the three categories of translation, it is our aim to show the relevance of going beyond those categories in light of a short case study undertaken in the context of the media's discourse in Quebec concerning the management of cultural diversity. It is in this sense that we are submitting, in this article, the outline of an ongoing reflection pertaining to a fourth category of translation, which we will call “inter-referential 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 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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.036 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".