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Enregistrement W1912281920 · doi:10.3233/wor-2011-1237

Narrative Reflections on Occupational Transitions

2011· article· en· W1912281920 sur OpenAlexaffabout
Rhysa Leyshon

Notice bibliographique

RevueWork · 2011
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueEducation Systems and Policy
Établissements canadiensWestern University
Organismes subventionnairesnon disponible
Mots-clésNarrativePsychologySociologyArtLiterature

Résumé

récupéré en direct d'OpenAlex

Mary’s story is one that is likely being repeated around the globe in the wake of the recent economic downturn. The program Mary refers to in her narrative is called Second Careers, run by the Ontario Government. The program was set up specifically for Ontarians who have been laid-off to train for new careers in high demand areas. Applicants are eligible for up to $28,000 (Canadian) in financial aide to use towards community college-level educational programs. This is Mary’s story of her occupational transition during this major economic recession. I finished high school but at the time never really thought about having a career. It was just all about having fun back then. I had no thoughts about my future. I know I was very lucky to get the job I did. It was because I had family and friends already working at the plant; that’s the only reason I got hired. It sure wasn’t because of my resume. I was aware when I was hired that things were different from when my parents worked. I knew I would not be working there for my whole life, but at the same time it wasn’t something I really thought about in terms of planning what I would do. Even when I started work, the future of the plant was on shaky ground. I worked at a manufacturing plant and earned a decent living. But in August of 2008 things changed quickly and initially for the worse. When the plant starting giving out pink slips I had no seniority so I was one of the first to go. I had worked for three years. I knew I had had it good but now there were just no jobs. Even fast food places weren’t hiring. And I couldn’t compete even if there were jobs. I didn’t have any special skills, so if it came down to me or someone with more education I knew that without personal contacts there was no way I would get hired. And most of my family and friends were soon in the same boat as me; they had all been laid off when the plant closed for good a few months later. It was very fortunate that I had worked for those three years though. I admit I was someone who complained loud and often about taxes, union dues and all the other money that gets taken from each paycheck. I have a very different outlook on that now. I was able to live on the employment insurance money at first but I knew it wouldn’t last very long. I have two children and I wanted them to have the life they deserved. I wanted them to be able to do things without having to think about money. I would have had to give up my car and maybe my house. Plus the prospect of finding another decent job was beyond unlikely. Manufacturing was not going to make a big comeback even when and if the economic situation turned around. Those jobs were gone for good. Through a centre set up by my old union I learned about a program run by the provincial government. The program is for laid off workers to get training in some skill or career at a college. I took this test that tells you what kind of work you would be good at and are interested in. The idea of taking something I was interested in was appealing. I was given enough money to go to school but more importantly I knew that at the end of the program I would have access to a career as opposed to just a job. I am about halfway through the program now and I have such a great outlook. The school helps us to find jobs and there seem to be quite a few. The last few months of the program are a co-op where the students work at a company to learn more practical skills. We are told that most students end up getting hired fulltime by the company they do the co-op for, so that’s encouraging.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,847
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,223
Tête enseignante GPT0,453
Écart entre enseignants0,230 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2011
Routes d'admission2
Résumé présentoui

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