Bibliographic record
Abstract
Our goal is to distinguish between the following two hypotheses: (A) The Internet will remain disproportionately in English and will, over time, cause more people to learn English as second language and thus solidify the role of English as a global language. This outcome will prevail even though there are more native Chinese and Spanish speakers than there are native English speakers. (B) As the Internet matures, it will more accurately reflect the native languages spoken around the world (perhaps weighted by purchasing power) and will not promote English as a global language. English's "early lead" on the web is more likely to persist if those who are not native English speakers frequently access the large number of English language web sites that are currently available. In that case, many existing web sites will have little incentive to develop non-English versions of their sites, and new sites will tend to gravitate towards English. The key empirical question, therefore, is whether individuals whose native language is not English use the Web, or certain types of Web sites, less than do native English speakers. In order to examine this issue empirically, we employ a unique data set on Internet use at the individual level in Canada from Media Metrix. Canada provides an ideal setting to examine this issue because English is one of the two official languages. Our preliminary results suggest that English web sites are not a barrier to Internet use for French-speaking Quebecois. These preliminary results are consistent with the scenario in which the Internet will promote English as a global language.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".