Present Thinking About the Future of Intellectual Property: A Literature Review
Bibliographic record
Abstract
This article presents the results of a systematic review and analysis of the way that “the future” is addressed in intellectual property literature. Iterative methodical searches in key databases of published materials and targeted reviews of grey literature revealed a limited number of relevant works pertaining to the future. These works were analysed and classified according to our original taxonomy, considering for example: whether the future was conceived as predictable or uncertain; whether the analysis was issue-specific, IP-categorical, or systemic; and whether the work considered legal, economic, technological, social, environmental, or ethical factors driving change. Quantitative and qualitative analyses of the literature demonstrates very few works that consider multiple factors driving systemic changes in an uncertain future. The article describes and recommends the use of distinct research tools, specifically foresight and scenarios methods, capable of addressing this gap in our present thinking about the future of intellectual property.
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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".