ABS: Big Data, Data Sovereignty and Digitization
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
This chapter focuses on the increasing sophistication of research practices through the applications of digitization and other aspects of information and communication technology (ICT). Multiple factors, including advances in biotechnology and the production, utilization and malleability of valuable research data through the use of digital technology tools have resulted in the transformation of data or genetic information into widely accessible virtual resources that are practically de-linked from their origins. Given the orientation of the Nagoya Protocol towards the physical transfer of genetic resources, the virtualization of Indigenous research data makes the latter part of the big and open data grab threatening the realization of ABS. However, despite the potential to de-link genetic resources (GRs) and associated traditional knowledge (aTK), including other aspects of Indigenous research data from their sources, conceivably, there are significant bases in the texts of CBD and the Nagoya Protocol for the inclusion of digitally sequenced data as part of ABS. Further, the interface of Indigenous peoples and local communities’ (IPLCs) nascent interest in data sovereignty and the big and open data phenomena provide an opportunity to apply critical data analytics to mainstream data equity as an integral aspect of Indigenous-sensitive ABS in an increasingly sophisticated and technology-driven research environment.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.005 | 0.011 |
| 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