Ethno-linguistic groups during an economic recession:Low-income earners in the 1990s'
Bibliographic record
Abstract
Little is known about low-income earners in the Swedish-speaking community in Finland, and particularly how this ethno-linguistic group positioned itself as compared with the Finnish speakers during the severe economic recession in the 1990s. Relating to the ethno-linguistic English-speaking minority in Quebec, we set out to study whether also Swedish speakers experienced a worsening of their economic position. Using register data from 1987-1999, we find that they did not, but rather improved their relative situation as compared with the Finnish speakers, although they on average had a higher propensity for being low-income earners also after the recession. In contrast to the situation in Quebec, no unfavourable language acts or educational reforms were imposed on the Swedish speakers during the study period. We see the results as reflecting a well-functioning welfare state, in which language acts and constitutional rights have worked to protect both ethno-linguistic groups.
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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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".