Bibliographic record
Abstract
This data compares Australia's performance in broadband against the other 30 OECD nations (at Dec 2018). A graph ordered by count of subs over 100Mbps puts Australia in the bottom two (since we have no over 100Mbps at Dec 2018). A calc of weighted average puts Australia in the bottom 3 of 30 OECD countries, just ahead of Colombia and Mexico, behind Chile, Turkey and Austria. OECD data available at: OECD Broadband Portalhttps://oecd.org/sti/broadband/5.1-FixedBB-SpeedTiers-2018-06.xls Counts are measured in subscriptions per hundred people. SPEEDS>1.5/2 Mbps (megabits per second)>10 Mbps>25/30 Mbps>100 Mbps The categories seem to mean;1.5 - 10Mbps | 10-30Mbps | 30-100Mbps | >100Mbps. A graph aggregates to three categories; slow (Under 30mbps), medium, 30-100 mbps) and fast (>100 mbps) This data is a response to the NBN sponsored report from Alpha Beta suggesting Australia is 19 / 37 countries and ahead of Germany, France and China. NBN report at: https://nbn.tm/Speed-Check Based on Dec 2018 #OECD DATA of internet speeds as reported by each of 30 countries - AU is at the bottom end of speeds, one of the few to report 0% over 100mbps eg France 5 subs/100 ppl over 100mbps; Germany 6; Spain 9, US 9; Canada 10. #nbn Calculation of weighted average speed of broadband users puts Australia 32 / 34 countries at 7mbps. Impacted by large number of under 25mbps services (27/33).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".