Bibliographic record
Abstract
This paper introduces a model for conducting research on living conditions among peoples that have experienced rapid social, cultural and economic change in countries where a non-parallel development has occurred. This model was developed by the researchers of SLICA, A Survey of Living Conditions in the Artic; Inuit, Saami and the Indigenous Peoples of Chukotka , which was initiated by Statistics Greenland in 1997. The point of departure for this model is a critique of contemporary living conditions surveys carried out by national statistical bureaus in economically, technologically and culturally segmented areas. The point of view is that these studies erroneously assume that the populations they investigate are homogeneous, and that consensus concerning individual social and economic objectives exists. This usually leads to research designs and indicators of individual well-being that reflect the dominant culture, or the prevalent way of living and thinking in these countries. The focus of this paper is on the research design of SLICA. The implementation of two important methodological challenges is discussed. Namely, (1) how to secure a contextspecific concept of well-being which also mirrors the life forms and the priorities of the respondents and (2) how to measure impacts of structural change on individual well-being. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".