Discrimination in Individual-Related Employment – A View from Europe and Germany to Canada, analysing the Requirements and the Background of the European Anti-Discrimination Directives
Bibliographic record
Abstract
Sometimes it seems that the EC directives could be a framework for the Canadian anti-discrimination system, too. They open ways to create procedures which are working for a long time in Canada. This does not mean this can be adopted in EC member states because there are too many peculiarities amongst domestic legislation. The problem seems to combine the directives, formed by influence from the North American legislation,130 with the European understanding of law. Especially in Germany, where employees are well protected by the law, the well-balanced system of rights is in danger. This danger does not seem to be banished by the new AGG which adopted many regulations of the directives without giving answers to questions of its implementation. But seeing the practice in other countries could bring thought-provoking impulses for the embodiment and the use of anti-discrimination law.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".