Legal and Ethical Issues around Incorporating Traditional Knowledge in Polar Data Infrastructures
Bibliographic record
Abstract
Human knowledge of the polar region is a unique blend of Western scientific knowledge and local and indigenous knowledge. It is increasingly recognized that to exclude Traditional Knowledge from repositories of polar data would both limit the value of such repositories and perpetuate colonial legacies of exclusion and exploitation. However, the inclusion of Traditional Knowledge within repositories that are conceived and designed for Western scientific knowledge raises its own unique challenges. There is increasing acceptance of the need to make these two knowledge systems interoperable but in addition to the technical challenge there are legal and ethical issues involved. These relate to ‘ownership’ or custodianship of the knowledge; obtaining appropriate consent to gather, use and incorporate this knowledge; being sensitive to potentially different norms regarding access to and sharing of some types of knowledge; and appropriate acknowledgement for data contributors. In some cases, respectful incorporation of Traditional Knowledge may challenge standard conceptions regarding the sharing of data, including through open data licensing. These issues have not been fully addressed in the existing literature on legal interoperability which does not adequately deal with Traditional Knowledge. In this paper we identify legal and ethical norms regarding the use of Traditional Knowledge and explore their application in the particular context of polar data. Drawing upon our earlier work on cybercartography and Traditional Knowledge we identify the elements required in the development of a framework for the inclusion of Traditional Knowledge within data infrastructures.
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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 | 0.135 | 0.162 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.095 |
| Scholarly communication | 0.028 | 0.034 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".