Extraktivt våld och urfolks koppling till mark
Bibliographic record
Abstract
Denna artikel är en omarbetad version av en presentation som hölls av författarna vid konferensen La Responsabilité de Protéger. Écologie et Dignité (The responsibility to protect. Ecology and dignity) vid Université Laval i Quebec i början av oktober 2017. Kristina Sehlin MacNeil och Niila Inga lärde känna varandra som forskare och forskningsdeltagare under Kristinas avhandlingsarbete, vilket avslutades i februari 2017. De har sedan dess inbjudits att tala tillsammans vid en rad olika konferenser. Detta är deras första gemensamma publikation, samt den första artikeln på svenska som behandlar Sehlin MacNeils resultat från hennes avhandling Extractive Violence on Indigeneous Country (2017), där hon med utgång i begreppet ”Extraktivt våld” diskuterar om urfolks perspektiv på marken och på kopplingen mellan människa och mark.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.137 |
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; both teacher heads agree on what is shown here.
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".