The experience of traineeships in the EU
Notice bibliographique
Résumé
Technical specificationsQuestionnaire Tables FLASH EUROBAROMETER 378 "The experience of traineeships in the EU" 5 KEY FINDINGSAround half of respondents report having had a traineeship (46%). More young people report having had a traineeship (46%) than an apprenticeship or student job (both 26%).Only a third of young people have had none of these experiences (32%).Traineeships are most common in Cyprus and the Netherlands (both 79%) and least common in Lithuania and Slovakia (both 8%). Respondents in France (41%), Finland (36%) and the Netherlands (31%) were most likely to have had more than three traineeships. Almost six out of ten respondents did not receive any financial compensation during their last (i.e. most recent) traineeship (59%). Among those who received financial compensation, less than half say this financial compensation was sufficient to cover basic living costs (46%). The highest number of respondents to receive financial compensation is in Slovenia (81%) while the lowest is found in Belgium (19%). Around three quarters of respondents said they were covered by insurance (73%).The majority of trainees think that their experience was or would be useful to find a regular job (71%), but more than one in four disagree (28%). The share of those who felt the traineeship helped or would help finding a job was highest in Romania and Ireland (both 85%) followed by Belgium, Spain, and Portugal (all 83%), while the share of those who felt that their last traineeship did or would not help finding a job was highest in Poland (43%), Cyprus (37%), Lithuania (36%) and Germany (34%).A high share of respondents has had multiple traineeships. Amongst those that have had at least one traineeship experience, 38% answered that they had only one traineeship, 22% had two, 14% had three, and 21% had four or more traineeshipsThe quality of the last traineeship was often better than that of another previous one 3 . In particular, while 10% of respondents said that they did not learn things that are useful professionally in the last traineeship, almost one in five (18%) report the same for another one undertaken previously. A similar pattern is visible in another question: while in the last traineeship 9% of respondents could not turn to a mentor that helped them and explained how to do the work, this share rises to 11% for a previous traineeship.3 Respondents who had more than one traineeship were asked more questions about one of their other traineeship experiences, which was randomly chosen.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,000 | 0,005 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».