Translating Cultural-Historical Psychology: Comments from Lay Professional
Bibliographic record
Abstract
In translating texts for the Chess Overall Development Project (Zaretskii, 2016), we have encountered several types of challenges that may be illustrative of what translators in the field of cultural-historical psychology (CHP) may deal with. Translators use various tools and strategies in their search for equivalence. Lack of the uniform CHP vocabulary and consensus on the CHP terms: di˙erences in transformational techniques and levels of the translators’ linguistic competence and their competence in CHP as such, result in co-existence of various translations of the same concepts, which may interfere with the process of communication and become a subject of controversy. Other challenges relate to specific linguistic features of the psychological scientific discourse of CHP, i.e. the need to observe rigorous scientific requirements to style and content, and abundance in expressive, emotionally and culturally charged utterances and vocabulary. The CHP terminology is characterized by specific word formation; lack of stylistic neutrality and lack of equivalent terms in target languages. Therefore, an appropriate translation implies using a special modification technology to create a target-language term which would have an equivalent denotative meaning; meet the requirements of the scientific style and preserve its stylistic uniqueness, emotional, and cognitive relevance (ensuring congruence of the reader’s experience with the author’s experience as mirrored by the lexical unit).
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 | 0.037 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.025 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.017 | 0.029 |
| Insufficient payload (model declined to judge) | 0.006 | 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".