Case Study: Indigenous Knowledge and Data Sharing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The IDRC-funded project 'Empowering Indigenous Peoples and Knowledge Systems Related to Climate Change and Intellectual Property Rights' is part of the Open and Collaborative Science in Development Network (OCSDNet). The project “examiners processes of open and collaborative science related to indigenous peoples’ knowledge, climate change and intellectual property rights”. Natural Justice, the lead organisation has a strong ethical stance on the agency and control over knowledge being vested with the contributing project participants, communities of the Nama and Griqua peoples of the Western Cape of South Africa. The project focuses on questions of how climate change is affecting these communities, how do they produce and maintain knowledge relating to climate change, how that knowledge is characterised and shared (or not) with wider publics, and how legal frameworks promote or hinder the agenda of these indigenous communities and their choices to communicate and collaborate with wider publics. Indigenous Knowledge is an area where ethical issues of informed consent, historical injustice, non-compatible epistemologies and political, legal, and economic issues all collide in ways that challenge western and Anglo-American assumptions about data sharing. The group seeks to strongly model and internally critique their own ethical stance in the process of their research, through for instance, using community contracts and questioning institutional informed consent systems.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it