Bibliographic record
Abstract
This paper is an attempt to investigate the current problems the students graduating in English at the ISLT 1 are likely to encounter when setting out to render English into Arabic. My teaching experience with them was beneficial, albeit quite short (one year-long only, 2000-2001). The material gathered, on the other hand, was wide-ranging and, better still, so provocative that I readily agreed to venture onto dangerous ground. 2 Studies in the past have often failed to delve deep into possible meanings and extend beyond traditional boundaries so as to assess the scope of words and explore the meaning potentials. Recent advances in the literature argue that translators should be sensitive to the losses and gains of cultural elements and assess the “weight” of these elements in the source text in order to bring about the same/similar effects. It is true that loss of meaning is inevitable and the transference to the translator’s language can only be approximate (Newmark 1988, 7). The current trend in translation theory is to explore situations to make it possible to transcend linguistic as well as cultural barriers. Translators will continue to reproduce only restricted facets of meaning so long as they do not vanquish ordinary processes of thought and approach the words in the SL text as units of discourse. I make no pretence at being able to offer definitive solutions. This account aims at identifying the potentially problematic areas in translating English into Arabic. The sense of new in this experience embodies a larger vision, apparently a different quality of recognition since the focal interest is laid on the interpretive weight of words as constituent parts of the act of communication.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.011 | 0.025 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".