Bibliographic record
Abstract
This paper comments on ‘Entrepreneurship and the Dot TV Phenomenon’ by Baldacchino & Mellor (2015) who suggest that state-run entrepreneurship is behind the success of .tv. To examine this, I briefly review the early years of .tv, the government’s administration of .tv, the actual impact of .tv income, and the numerical weight of .tv income compared with other sources of government revenue. I debunk several .tv-related myths and explore the media’s enduring .tv attraction. I also comment on topics covered by the authors that are unrelated to .tv – such as subsistence, exports, development models – identifying inaccuracies, issues in need of clarification, misleading descriptions, or material that I find stretched beyond credibility. Connecting .tv success to entrepreneurship might be a reasonable premise, but I doubt its plausibility. Notions of entrepreneurship, however, can be conceptually different. Perhaps this could be a starting point to re-examine such differences, some of which can be slender, in the context of island- and sovereignty-related assets and income.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.021 | 0.023 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".