Efficiency of Public Promotions Policies in the Diffusion of Broadband Networks: An Exploratory Analysis
Bibliographic record
Abstract
Extant literature on broadband diffusion at a country or regional level focuses on different research strands. The dependent variable in these works is based on the explanation of the acceptance or the diffusion of broadband networks. Research activity has been focused on the relationship between the supply-side and the demand-side evolution from the point of view of potential users, on the effect of value-added services and technological complements on the diffusion of broadband, and on the benefits that broadband can provide to areas such as education, health services, employment and culture. Finally, research activity can also be found in the influence of technical characteristics or attributes related to specific market segments in countries with greater broadband penetration. High interest has been spent on the analysis of the deployment of broadband networks in countries with scarce economic and technological resources. In developing countries, information and communication technologies have been postulated as a main drive for economic and social development. Giving access to information and to broadband network infrastructures, digital divide can be avoided and sound steps can be taken to accelerate the technological evolution and to reach higher levels of international competitiveness. Countries with high broadband penetration, like Korea, Japan, Canada, and Scandinavian countries, are good examples of strong economic and social development and they are used as a reference for developing countries.On the other hand, the commitment of public administration in broadband dissemination forces the promotion of the diffusion of broadband networks through public policies. In this vein, this work analyzes if public promotion policies are a relevant factor in the success of broadband diffusion in specific countries of the European Union and other parts of the world.The main goal is to determine the relationship between diffusion policies and the efficiency in terms of acceptance and use by individuals. This can help public decision makers when issuing new programs or proposing new policies.The study is based on an empirical analysis of current broadband promotion programs. The work method consists of compiling information about promotion policies. More than one hundred programs have been collected. Compiled information has been classified and standardized in order to homogenize the data and quantitative methods have been applied to perform an exploratory analysis. Macroeconomic data has been included in order to characterize the behaviour of the countries. Cluster analysis techniques have been used in the exploratory phase of the study. Primary results are outlined in the paper.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".