How fast is Australia's broadband (vs OECD) - 2018?
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 presentation titled; "Maximising the Value of the NBN; comparing our NBN to Australia's Top 10 Trading Partners and the OECD", attached for presentation 25 Feb 2020, Telsoc event (link added). Event Title: NBN Futures Forum - Learning from International Experience (Description added). Melbourne. Version: 8. Draft v3 of slides (18.2.20).7. Seminar 25/2/20 (link added); Slides Draft v1, v2 slides added (pdf).6. Added pdf of Submission (Sub_016_RF.pdf) to Inquiry into NBN, and link to published submission. (Appendix 1 links not working). A separate Appendix 1 with working links now attached.4/5. Uploaded cleaned up Graph, with better legible x-axis. Countries marked with * are Australia's Top 10 Trading Partners - exports, imports.3. (Jan 2020) - adds China, Singapore who are non-OECD, and Australia 2009 (Source ABS 8153.00).Add filter, to allow comparing - all OECD (plus China, Singapore), filter = 1 (top 10 AU trading Partners), filter = 2 (AU 2009, 2018, post build NBN). Added slower speed for AU 2009.China estimate of slower speeds, based on s-curve take-up of high speed, see CNNIC Report (linked below). Singapore data - source to add. 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. ERRATA; Noted calc error in V2 of average speed. Corrected in V3. OECD data available at: OECD Broadband Portalhttps://oecd.org/sti/broadband/5.1-FixedBB-SpeedTiers-2018-06.xls NB: OECD should be an author of this dataset, but Figshare currently doesn't allow OECD to be included as an author, since they are not a known author in the Figshare system. Please cite as Ferrers, R, and OECD (2019). Title etc... 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 | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".