Notice bibliographique
Résumé
In 2013, two authors of this book, Maggie Walter and Chris Andersen, published the original Indigenous Statistics: From Data Deficits to Data Sovereignty . These two scholars, one palawa, from Tasmania, Australia, the other Métis, from Saskatchewan and living in Alberta, Canada, met as board members of the then nascent Native American and Indigenous Studies Association (NAISA). Their shared interest in quantitative analysis led first to a recognition of their common experiences as Indigenous academics pursuing scholarship using primarily quantitative methodologies. Discussions around these similar experiences led to collaboration around their scholarship built around a shared understanding of the similarity of their experiences. The book they wrote from these was built around three central premises: Statistics are culturally embedded phenomena rather than neutral data All statistics are, in one way or another, culturally embedded rather than acontextual or neutral numbers. As such, Indigenous statistics reflect the purposes, assumptions and interests of those who have the power to commission, collect, analyse, interpret and disseminate the data, rather than necessarily reflecting the more robust complexity of Indigenous lived realities. For Indigenous Peoples in Anglo-colonized nations (Australia, Canada, Aotearoa New Zealand and the United States), the common trope of these data is one of deficit . The narratives that accompany these data have defined and continue to define, pejoratively, the relationship between Indigenous Peoples and their respective nation-states. The methodology, rather than the statistics themselves, are what create culturally “loaded” data Methods and methodologies are not interchangeable terms. Methods are the mechanisms through which data (in this case, statistics) are collected and analysed. Methodologies are the overall approach that shapes the research: what is considered worth doing; the underpinning assumptions; the key question/s asked; of whom; and why; and the framework through which the data are interpreted. Methodology, unlike method, therefore has almost nothing to do with data and everything to do with the socio-cultural positioning, value systems, knowledge systems and lifeworld of the researcher/data commissioning entity. Almost without exception (until recently, at least), for Indigenous statistics, that researcher/data commissioning entity has been non-Indigenous. Indigenous-led research shares similarity of methodology and legitimacy barriers This premise posits that all Indigenous researchers need to be more cognizant of the translative processes through which knowledge is translated into and out of the academy. This point was aimed, in part, at redressing the pointless, but often vigorously pursued, argument that quantitative research is culturally antithetical to Indigenous Peoples. Automatically positioning all quantitative research as positivist in approach, this claim asserts that such research is unable to reflect the culturally complex social relations—the lives and lived experiences, in other words—of Indigeneity. From our methodology-not-method premise, however, we know that it is methodological approach rather than the means of data collection that underpins the social meaning of research. Thus, Indigenous research that is framed by Indigenous perspectives and lifeworlds have more methodological similarities than differences, regardless of method.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,008 | 0,005 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,356 | 0,213 |
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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».