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Record W7028334181

Evaluating Governance and Market of Country Code Top Level Domain (ccTLD): Lessons for Indonesiaâs ccTLD .id

2013· article· en· W7028334181 on OpenAlexaboutno aff

Bibliographic record

VenueScholarWorks@UMassAmherst (University of Massachusetts Amherst) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCybersecurity and Cyber Warfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsThe InternetCorporate governanceGovernment (linguistics)Internet governanceCorporationDomain (mathematical analysis)Code (set theory)Stakeholder
DOInot available

Abstract

fetched live from OpenAlex

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 Internet’s 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.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0050.007
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.050
GPT teacher head0.305
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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