Framgångsfaktorer på bredbandsmarknaden – vad utmärker en framgångsrik marknad?
Bibliographic record
Abstract
This master thesis is written at the Royal Institute of Technology during the spring and summer of 2008. The assignment was to investigate what factors on a broadband market determine how well it performs in terms of how many people have access to broadband. These are the factors that determine whether or not a country or market will be able to achieve a high broadband penetration. The purpose of this thesis was to establish a better understanding for what a market should look like or how a struggling one can be complemented in order to succeed. As a foundation for this investigation, a group of benchmark countries were chosen and evaluated. In addition to this, a group of, developing MENA 2 -countries were chosen and evaluated. Finally, interviews were performed with some of Sweden’s leading internet service providers, the regulatory agency and infrastructure company, Skanova. Among the benchmark countries were Sweden, France, Canada, South Korea and Japan. The MENA countries included Egypt, Iran, Qatar, United Arab Emirates, Jordan, Turkey and Saudi Arabia. The investigation included statistics from various sources, reports from analyst firms, as well as theories from literature and discussions with Ericsson employees. All the countries in the MENA study turned out to have very varying situations which made it easier to distinguish effects of different circumstances. Many factors affect the penetration levels in different ways. For prices to match the market’s ability to pay, open competition must exist. The level of competition is closely affected by the level of regulation from an independent regulator, making sure no player abuses a position of unusual power. An understanding from the government of the benefits of broadband is invaluable for establishing a stable foundation. Government actions have proven successful in both raising PC penetration and building solid infrastructure.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.104 | 0.060 |
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".