Innovation, Intellectual Property and Development Narratives in Africa
Bibliographic record
Abstract
Human development, including not just economic growth but also the capability for longer, healthier and more fulfilling lives, depends on innovation and creativity. While various economic, technological, social and other factors influence innovative and creative activity, intellectual property (IP) rights – copyrights, patents, trademarks, trade secrets and other appropriation mechanisms – play an increasingly important role. How IP rights help or hinder innovation and creativity in different contexts in Africa is the subject of this book.This chapter canvasses aspects of the current reality of IP in the four main regions of the African continent. The evidence it summarizes helps to build an understanding of the ways in which the dual goals of protecting IP and preserving access to knowledge can be balanced. This chapter also gives an explanation of the broader rationale and methodological choices of Open AIR's research, which provides indications of the roles that are being, and can be, played by collaborative and openness-oriented dynamics in relation to innovation, creativity and IP. A better understanding of the nuances and dynamics of IP is essential to creating policy frameworks and management practices that balance IP protection and access in such a way that African regions, nations and communities can harness IP as a tool to facilitate collaborative networking within diverse systems of innovation and creativity.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.017 | 0.030 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".