Propriet� intellettuale e scienza aperta : il caso studio del Montreal Neurological Institute
Bibliographic record
Abstract
The aim of this paper is to understand if Open Science is able, in the digital age, to act as a valid alternative to traditional intellectual property rights (in particular patents and copyrights); these rights have characterized the protection of intellectual works so far. The reconstruction and the comparison between the principles of intellectual property and Open Science have given the chance to answer to this question. Above all, the study of an empirical case at the Montreal Neurological Institute in Canada was essential. This case represents the implementation of Open Science in the research on neurodegenerative diseases. The deep analysis of this model gave an affirmative answer to the initial question, since the open sharing of data, experiments and research results, allowed a strong acceleration of innovation. Among the principles characterizing the canadian institute, as a matter of fact, there is the prohibition to patent any kind of invention or result. The main objective is to give the possibility to local companies to develop new drugs and treatment of neurological diseases in order to save lives in present and future generations. These facts demonstrate the efficiency and benefits of Open Science. Open Science is a recent movement, which has developed with the birth of the Internet; it aims to eliminate any kind of obstacle (legal, economic or technological) to the dissemination of science and knowledge. This is possible because research results, scientific material and data are made available to the public. In order to deepen this theme, an entire chapter of this paper is dedicated to the description of Open Science through its definition, historical evolution, analysis of the benefits and disadvantages that derive from its application and from the explanation of the legislative current framework. Since this movement is opposed to intellectual property rights, another chapter of the paper illustrates its general characteristics and describes the most common tools (patents, copyrights and trademarks), both at international and canadian level (since Montreal is the town where the case study takes place). Throughout the paper, a special attention is given to the academic research and the role of the university itself, underlining its recent tendency to ?commodify? the knowledge produced within it and to pursue profits in the same way as industries, using intellectual proprietary tools. The result of the thesis leads, on the contrary, to assert that universities need to rely on the Open Science model and they shoul let the knowledge, developed within them, spread among as many people as possible. In this way, it will be possible to fulfill the so-called ?third mission? of the university, that is the transfer of knowledge considered as a common good.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".