8. Why is it so Challenging for Adults to Acquire a Second Language?: An Evolutionary Perspective
Bibliographic record
Abstract
Many aspects of human behaviour and human phenomena can be analyzed using Charles Darwin’s theory of evolution by natural selection. Darwin’s theory states that the traits present in human ancestors that left behind the most descendants are the very traits that are passed on to future generations. The trait that will be examined in this poster is second language acquisition. It will explore the reasons why it so difficult for adults to learn and master a second language. Our earliest ancestors had no linguistic ability; therefore, we developed the anatomical features that allowed for spoken language. These changes allowed for rapid language acquisition in young children up until adolescence, but did not support such language acquisition in adults. Our evolutionary past as nomads and hunter-gatherers ultimately explains this phenomenon. The nomadic society of humans was small in size, which led to a lack of inter-cultural contact. There was thus no incentive to learn to communicate with individuals whose language differed from one’s own. In addition, the short life expectancy of our ancestors left little time to become fluent in another language. Finally, in situations of inter-cultural contact, the most violent group prevailed. It is important to understand that the challenges of adult language acquisition have an evolutionary basis because it will allow us to design effective language acquisition techniques.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".