Oceanic Environmental and Indigenous Data: Reconciliation of Indigenous Data Governance and Sovereignty within Environmental Data Repositories
Notice bibliographique
Résumé
Ocean Networks Canada (ONC) straddles the nexus between community relations, infrastructure installations, and data storage. As such, ONC has opportunities to partner with Indigenous communities and incorporate CARE Principles (Collective Benefit, Authority to Control, Responsibility, Ethics) from the point of engagement through to implementation and curation of Indigenous data. To further our commitment to CARE, ONC is implementing Local Contexts label compatibility into our metadata and dataset architecture. Local Contexts can help Indigenous communities reassert their cultural authority to dictate to users how their own data are to be collected, managed, displayed, accessed, and used (Local Contexts, 2024) while hosted on ONC’s data repository. Data agreements are binding agreements between ONC and our various partners – including Indigenous communities – to establish conditions and rules for our continued collaborations. Sensitive datasets can be restricted at the discretion of the community, and a pathway exists in which data users can make requests if they wish to pursue access to the restricted data. ONC as a custodian and distributor does not hesitate to relinquish ownership of data to further promote Indigenous data sovereignty and governance. A stake in ownership of community-based monitoring data is not a necessity for ONC but is rather a discussion to have with our Indigenous partners. License and Policy attribution to the data can be decided during negotiation of the data agreement between parties, where we can then apply Creative Commons (2024) Licenses or other licenses as deemed fit by the community. These processes are set during the consultation phase, however. Indigenous communities are dynamic, and their needs can change over time. What happens if a community wishes to change one of these previously agreed-upon rules? Local Context Labels are one way for Indigenous communities to flex their authority to control the data which they own. When a community changes their project labels, it will get picked up in ONC’s dataset metadata and landing pages. In practice, this is one method communities can leverage to prompt new amendments of agreements by ONC. These labels can also be used by communities to express traditional heritage, biocultural phenomena, or other notices of significance directly to the end-user. In a pilot project, ONC will develop the software architecture to support Local Context labelling. Using guiding documents and best practices, ONC will implement this in our underlying metadata profiles such DataCite Fabrica and ISO19115. Further software development will make labels visible in our Dataset Landing Pages (DOIs). The labels will be inherited directly from the Local Contexts application programming interface (API) of projects associated with our Indigenous partner communities. Since there are currently no partner communities using Local Contexts, ONC will also design a “mock community” with ONC-owned data to provide tangible examples of Local Contexts dataset integration to our partners and the broader scientific community. Should the pilot project be deemed successful, ONC will then fully implement Local Contexts compatibility with our production version of ONC’s Oceans 3.0 Data Portal. Communities ONC engages with will then have the option to utilize Local Contexts to further their own Indigenous data governance and sovereignty.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,100 | 0,154 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,008 | 0,011 |
| Études des sciences et des technologies | 0,011 | 0,025 |
| Communication savante | 0,029 | 0,037 |
| Science ouverte | 0,006 | 0,037 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».