Annotated Translation: Daniel Levitin, This Is Your Brain On Music: The Science of a Human Obsession; UK: Penguin Group 2006, chapter 4
Bibliographic record
Abstract
The aim of this bachelor's thesis is to translate the fourth chapter of the popular science publication called This Is Your Brain on Music: The Science of a Human Obsession from English into Czech. The book deals with the effect of music on the human brain, it was written by Daniel Levitin, a Canadian-American author, and published in 2006 by Dutton (Penguin Group). The thesis also includes a commentary on the overall translation process and analysis and everything that a translator must attend during their work. In addition to the translation itself, the thesis therefore includes the analysis of the source text, as well as the translation method and fictional assignment, selected translation problems and their solution through translation techniques and shifts. Key words translation, commented translation, translation analysis, music, cognitive psychology, neuroscience
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.152 | 0.053 |
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".