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Enregistrement W1834717837 · doi:10.19154/njwls.v4i4.4704

Factoids of Working Life

2015· article· en· W1834717837 sur OpenAlexaboutno aff
Jan Ch. Karlsson

Notice bibliographique

RevueNordic Journal of Working Life Studies · 2015
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueLabor Movements and Unions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésnobodyPublic lifeWorking lifeSociologyLawPolitical sciencePoliticsComputer scienceEpistemology

Résumé

récupéré en direct d'OpenAlex

Factoids are statements that are repeated so often that they appear as facts even though they are not. I don't know any field that is as full of factoids as working life. And-sadly-there are working life researchers contributing with factoids to what is called the public debate.When I detect factoids of working life, they have one thing in common: They are portrayed as self-evident truths, but they do not have any data to back them up. For example, it was not long ago when this had become a fact: People in general and youth in particular don't want secure employment any more. Earlier, in the old society, people wanted such security, but now, in the new society, nobody wants it any longer. But the statement was false. It was a factoid. It was spread in mass media, most politicians believed in it, and there were even working life researchers who disseminated it. But it was false. It was a factoid. For those who wanted to see, there were numerous empirical studies of what people really thought about secure jobs. They showed clearly that secure jobs were what people wanted and that there was no difference between age groups. But in the public debate, facts succumbed to the factoid.Another example: Labor law, especially the Law on Security of Employment, is stricter in Sweden than in other countries and it curtails the rate of employment. It is false. To begin with, Sweden belongs to a middle category of strictness, that is, the degree of security of employment, in international comparison. At the top of the table of strictness, we find France, Portugal, Turkey, and Spain. At the bottom are for example Japan, Denmark, Canada, UK, and USA. And among those in between are Sweden, Norway, Finland, Germany, and Poland. Further, if the statement was true, those countries that have liberalized their labor market laws should have reached a higher level of employment than other countries. But that is not the case. There simply is not any correlation between the strictness of labor law and the degree of employment (Furaker, 2009; Furaker et al., 2007). The statement is a factoid.A further example: The demands of qualifications in working life have risen to such an extent that people's level of education has not managed to keep up. There is a widespread under-education on the labor market. It is false. In reality, it is the other way around. It is true that there is a big gap between individuals' level of education and the qualification level of the jobs, but it goes the other way as it were: People's level of education has risen to such an extent that qualifications in working life have not managed to keep up. There is a widespread over-education on the labor market (le Grand et al., 2004; Tahlin, 2007). The statement is a factoid.It's hard to stop, here is another one: Equal pay threatens jobs: the smaller the wage gap, the lower the level of employment. Sometimes it is also expressed as a big wage gap is favorable to the number of jobs. But however it is formulated it is false. Let us have a look at the employment and wage differences in 22 countries during 22 years (Barth & Moene, 2012). If the statement was true, the level of employment should be lower in countries with a high degree of equality of wages. But it isn't like that. On the contrary, there is a stable pattern of the level of employment being higher in those countries-like the Nordic ones-that have the smallest wage differences. Further, if the statement was true, the percentage of the population in the labor force should be lower where wage equality is high. …

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,025
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
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 enseignants0,030
Score d'incertitude au seuil0,101

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,025
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0030,002
Études des sciences et des technologies0,0080,022
Communication savante0,0130,018
Science ouverte0,0010,008
Intégrité de la recherche0,0040,006
Charge utile insuffisante (le modèle a refusé de juger)0,0300,009

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 Opus0,233
Tête enseignante GPT0,371
Écart entre enseignants0,138 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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
Publié2015
Routes d'admission1
Résumé présentoui

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