Data Management Plan: Empowering Indigenous Peoples and Knowledge Systems Related to Climate Change and Intellectual Property Rights
Bibliographic record
Abstract
The 'Empowering Indigenous Peoples and Knowledge Systems Related to Climate Change and Intellectual Property Rights' project examines processes of open and collaborative science related to indigenous peoples knowledge, climate change and intellectual property rights. It assumes and challenges practices of open science as a process, one that should involve modes of being both open and closed. The project takes history into account when considering how indigenous peoples' are producing knowledge related to climate change and how such knowledge maybe characterized as indigenous peoples' intellectual property and/or impacted by dominant intellectual property regimes. The central questions the research is addressing are: (i) How is climate change impacting indigenous Nama and Griqua communities? (ii) How are these communities producing indigenous knowledge related to addressing climate change and offering alternative strategies? (iii)How do indigenous Nama and Griqua characterize their knowledge as indigenous intellectual property (or not) and decide to openly share their knowledge (or not) internally or with the outside public? (iv) How and what types of laws and policies (including intellectual property rights) promote and/or hinder these indigenous strategies and open collaboration with the public? The data are being collected and created to answer these main questions. Furthermore, the researchers are critically tracking the research process itself and this data will be scrutinized to provide information on the open and collaborative science process and dilemmas and tensions around openness issues.
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 | 0.033 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 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".