Bibliographic record
Abstract
Courtesy of the TRIPS Agreement and the monumental transformations in innovative technologies, especially in the bio and digital arenas, the global IP order has been fundamentally altered since a decade and a half ago. Not only has global awareness of IP increased, the latter's significance as a flashpoint of tension in regard to its practical impact on every sector of the global socio-economic and cultural constitutive process is felt now more than ever before. Consequently, IP is a subject matter for the global governance discourse. This article reflects on the failure, successes, accomplishments, challenges as well as the unsustainable nature of current global IP order. It argues that the current order has run its course. It is now time for change. Spotlighting the changing political and economic landscapes currently being re-drawn by emerging regional and global economic powers of the South, the article speculates on the future direction of global IP law and policy. These new economic and power blocs bear the seed or agency of the present urgency for a new approach to global governance of IP. At the core of that urgency is the imperative for mainstreaming equity, development imperatives and, overall, public regarding considerations in the current calibration phase toward a new global IP order.
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 | 0.031 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.102 |
| Scholarly communication | 0.024 | 0.038 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".