Bibliographic record
Abstract
Translation is usually viewed as a process designed to overcome a deficiency: X translates the words of A for C, because C doesn't have an adequate command of the language in which A expressed himself. Translation so practised is usually, if not always, an entropie process. As I think I have shown in a recent article, there is a strong tendency of the text to run downhill in translation, with a demonstrable loss of ordering and coherency, an inexorable regression of form to formants, of the marked to unmarked1. There exist, however, cases where translation ceases to be a mere expedient and, far from being entropie, conserves or even enhances the ordering of the texts it brings into play. It is in such cases that translation may be said to function heuristically, by foregrounding structures taken for granted in the source text, by making the information encoded in the text more readily accessible to the target-language group than it was to the source-language group, by enhancing the repertory of esthetic forms available to the target-language group, or by stimulating the creation of new forms. A number of more or less canonical examples come to mind immediately. Ethno-linguistic translation zeroes in on what I have referred to elsewhere as the grain of the text (i.e. the micro-structures that derive from the linguistic substratum2), in order to provide insights into the functioning of languages very different from that of the target group. In hermeneutic translation, the usually subterranean work of interpretation surfaces quite explicitly in the target text, which thus functions as a gloss, making the message more readily available to the target-language reader than it was to start with in the source text3. But the heuristics of translation can go far beyond the narrow scope of such undertakings.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.049 | 0.024 |
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".