Bibliographic record
Abstract
While what is said can be difficult to understand, what is not said may pose an even bigger challenge. Language is efficient, so often what goes without saying is simply not being said. It is left for the reader or listener to interpret underspecified language and resolve ambiguities, a task that we do seamlessly using our personal experience, knowledge about the world, and commonsense reasoning abilities. In many cases, commonsense knowledge helps EFL learners compensate for low language proficiency. However, what is considered “commonsense” is not always universal. Some commonsense knowledge, especially pertaining to social norms, differs between cultures. Can language technologies help bridge this cultural gap? It depends. Chatbots like ChatGPT seem to have broad knowledge about every possible topic in the world. However, ChatGPT learned about the world from reading all the English text on the web, which is primarily coming from the US, and thus it has a North American lens. In addition, despite being “book smart,” it still lacks basic commonsense reasoning abilities that are employed by us to understand social interactions and navigate the world around us.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".