Yes they can: an exprimental approach to the eligibility of ethnic minority candidate in France
Bibliographic record
Abstract
List of Tables List of Figures List of Contributors Series editor’s preface Preface Acknowledgements 1. Migration and Political Representation: An Introduction and a Framework - Karen Bird, Thomas Saalfeld, and Andreas M. Wüst Part I: Immigrants and Members of Visible Minorities as Voters: Turnout and Party Choice 2. Voter Turnout amongst Immigrants and Visible Minorities in Comparative Perspective 3. Party Choices amongst Immigrants and Visible Minorities in Comparative Perspective Part II: Immigrants and Members of Visible Minorities as Candidates for Elective Office 4. New Citizens – New Candidates? Candidate Selection and the Mobilisation of Immigrant Voters in German Elections - Sara Claro da Fonseca 5. Minority Representation in Norway: Success at the Local Level, Failure at the National Level - Johannes Bergh and Tor Bjørklund 6. Ethnic Inclusion or Exclusion in Representation? Local Candidate Selection in Sweden - Maritta Soininen 7. Yes They Can: An Experimental Approach to Eligibility of Ethnic Minority Candidates in France - Sylvain Brouard and Vincent Tiberj Part III: Immigrants and Members of Visible Minorities as Legislators 8. Minority Representation in the US Congress - Jason Casellas and David Leal 9. Patterns of Substantive Representation Among Visible Minority MPs: Evidence from Canada’s House of Commons - Karen Bird 10. Presence and Behaviour: Black and Minority Ethnic MPs in the British House of Commons - Thomas Saalfeld and Kalliopi Kyriakopoulou 11. Migrants as Parliamentary Actors in Germany - Andreas M. Wüst 12. Epilogue: Toward a Strategic Model of Minority Participation and Representation - Thomas Saalfeld, Andreas M. Wüst and Karen Bird\n\n
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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".