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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".