Tackle discrimination and encourage diversity
Bibliographic record
Abstract
Discrimination plays an important role in the persistent disadvantage faced by many youth with migrant parents. It has two distinct facets: individuals’ subjective perception of being discriminated against and actual discrimination, for example in the hiring process. Regarding the latter, applicants with a ‘a 'foreign-sounding name' often have to send twice as many applications before receiving a positive reply as their peers with otherwise similar CV but a “native‑born” sounding name (Heath, Liebig and Simon, 2013[93]). EU-wide, almost one in five youth with immigrant parents feels part of a group that is discriminated against; significant shares of self-reported discrimination are also found in other OECD countries, including Canada, Israel and the United States. In Europe, the share is higher among those whose parents are native‑born than among their foreign-born peers (OECD/EU, 2018[1]). While this does obviously not mean that the actual incidence is higher for the former group, it does point to a higher awareness of the issue.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".