Evaluating Governance and Market of Country Code Top Level Domain (ccTLD): Lessons for Indonesiaâs ccTLD .id
Bibliographic record
Abstract
Domain name in this term refers to the Internet Domain Name System (DNS), the hierarchical naming system of Internet Protocol (IP) resources. One of the Top Level Domains (TLDs) of this structure is the country code Top Level Domain (ccTLD), which identifies the location of each nation on the Internet, such as .fr, .us, .id.\nCountry code Top Level Domains (ccTLDs) are not just simple identifiers on the Internet but Internet entities that enable people and institutions to exist in virtual countries and space. Moreover, ccTLDs as one of the Internetâs resources have become a business that fosters utilization of the Internet especially e-commerce.\nThe Internet Corporation for Assigned Names and Numbers (ICANN) has started to call on all countries to reform the management of ccTLD to strengthen the government involvement in the ccTLD management system. Accordingly, since 2008, the government of Indonesia has initiated regulatory reforms to manage the Country code Top Level Domain Indonesia (ccTLD .id)\nThere are two main concerns questioned in this research: First, what is the model of governance for ccTLD. id? Second, what are the implications of the governance model to improve the market of ccTLD .id?\nThis research will use comparative method by analyzing secondary data to compare the similarities and differences between four national ccTLD regime models based on multi stakeholder system represented by four ccTLDs, which are .ch (Switzerland), .au (Australia), .mx (Mexico), .ca (Canada). The Analysis of four national ccTLD regime models focuses on model of governance and market orientation of each ccTLD.\nThis capstone is intended to suggest the governance model to support globalized market of ccTLD. ID. Because of the low level of the Internet penetration and inequality of ICT infrastructures among regions in Indonesia, ccTLD.id market should not be limited at national market, and the Internet users should be allowed to have direct access to ccTLD .id. Moreover, this research will define what the implications of governance are, in order to improve the market of ccTLD .id.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".