Toward a Crypto-Friendly Index for the APEC Region
Bibliographic record
Abstract
This paper presents a new index concerning the extent of public policy accommodation towards usage of blockchain technology. The coverage of the index is for the 21 Asia-Pacific Economic Cooperation (APEC) member states, representing a significant bloc of global production, trade and economic development. The crypto-friendly index includes indicators related to four general categories of blockchain policy: (i) extent of policy restrictiveness toward cryptocurrency initial coin offerings; (ii) extent of policy restrictiveness toward cryptocurrency exchanges; (iii) taxation treatment toward cryptocurrencies; and (iv) type and extent of general public policy interest in blockchain-related activity. Based on data and information available as at October 2018, the index results reveal considerable diversity exists amongst APEC countries in terms of their degree of crypto-friendliness. Jurisdictions such as Hong Kong, Singapore, Australia, the United States and Canada are seen as relatively crypto-friendly locations, whereas jurisdictions such as China, Vietnam and Peru have the greatest scope for pro-blockchain policy improvement. This paper suggests future avenues for index refinement, as well as the potential for additional research into the concept of crypto-friendliness using this and similar policy indexes.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.032 | 0.001 |
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; both teacher heads agree on what is shown here.
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".