The Power of Language: How the Codes We Use To Think, Speak, and Live Transform Our Minds
Bibliographic record
Abstract
It is estimated that approximately 60% of the world’s population knows and uses two or more languages. Given that multilingualism is the norm rather than the exception, at least on a global scale, Viorica Marian’s ‘The Power of Language’ is a timely and accessible book that intricately captures the complexity—and benefits—of multilingualism. Spanning 11 chapters and around 225 pages, the book is divided into 2 parts. In the six chapters of Part 1, Marian discusses how language influences individuals. Part 2’s five chapters explore how language exists and function at the societal level. The book finishes with a conclusion section. The cleverly titled Chapter 1, ‘Mind boggling’, serves as a fitting introduction to the marvels of the multilingual mind. It delves into the profound impact that learning a new language can have on an individual. Marian explores this by discussing recent research on eye movements, revealing how multilingualism enhances executive functions, focus, and cultural adaptability. The chapter examines both the tangible and psychological benefits of learning a new language, underscoring the notion that through language, individuals gain diverse perspectives, enabling them to navigate within their environments without the limitations of a single language system.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.045 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".