Bibliographic record
Abstract
It was 2005 and I was living in Copenhagen where I was enjoying my Erasmus year abroad.I saw a poster at the University of Copenhagen advertising a Migration Studies conference in Maastricht.I impulsively decided to go without even suspecting that this event would change my life forever!After almost a year in Denmark, full of immigration challenges and life-changing integration experiences, I had already made up my mind to focus my professional path on migration and refugee issues.My meeting with prof.Hildegard Schneider and the interactions with all the fantastic scholars that Maastricht University had brought together, gave me the final impetus to pursue a migration-related career.I did not realise that it was just a matter of time until my life path would bring me back to Maastricht.In October 2009, my friend and colleague Valeria Ilareva invited me to join the annual conference of the European Network on Free Movement that was coordinated by the Centre for Migration Law of the Radboud University in Nijmegen.Meeting prof.Kees Groenendijk there was a turning point in my future academic path.He read my first ever PhD proposal and encouraged me to pursue my research project that was devoted to circular migration policy.A couple of months later, prof.Groenendijk put me in touch with Hildegard and the rest is history!So many people from so many different places contributed to the successful accomplishment of this ambitious project, which has travelled throughout Europe like a circular migrant with a constantly changing trajectory.I would like to acknowledge all of them here.I would like to first thank to my first supervisor Hildegard Schneider who gave me the chance to pursue a PhD with her.I will never forget our second meeting at the hotel cafe facing the Gare Central in Brussels where she offered me the opportunity to start a PhD at Maastricht University as an external candidate and later on gave me the chance to work on my dream PhD project as part of TRANSMIC.Encouragement, inspiration, patience and trust are the qualities that make Hildegard such a great supervisor!I would like to thank her for always finding the time to talk to me, listen to my frustrations (academic issues, but also VIII those of a more personal nature), and reminding me that despite the fact that I am a PhD candidate, I should not forget to enjoy life!I would like to also thank her and René for opening the doors of their home to me so many times and for all the inspiring conversations that were held over a glass of wine.I owe a big thanks to my second
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".