Briefing No. 6 - Financial Inequalities and the Pandemic
Notice bibliographique
Résumé
Many households’ financial situation declined during the pandemic. 39% reported worse financial health, and just 16% better. Gaps have widened, with 52% of disadvantaged households reporting worse financial health, compared to 34% of others. 22% of professional households reported an improved financial situation, over twice that of working class households (10%). \n \nOne in ten young people (10%) were living in households classed as food insecure, with many reporting running out of food, skipping meals, and 5% of parents reporting going a whole day without eating. \n \nSocial renters were six times more likely to experience food insecurity than those who owned their home (26% vs 4%). Rates of food insecurity were highest in the North East and North West (15% and 12%), and lowest in the South East (9%) and East of England (7%). \n \n8% of parents used a food bank during the pandemic period, three quarters of whom had also used food banks pre-pandemic. Food poverty is not restricted to Free School Meals eligible families. The majority (57%) of households where children went hungry were not FSM eligible during that time, and 36% of those using foodbanks were not FSM eligible. \n \nPupils in families who reported using food banks during the pandemic received lower GCSE grades (almost half a grade per subject), even taking into account previous grades and other aspects of their household finances. However, long-term disadvantage played a bigger role than the pandemic. \n \nPandemic financial experiences were more closely linked to mental health. Among families finding it very difficult to get by financially, rates of psychological distress were 82% among parents, and 53% among children. Among parents this is four times higher than those living comfortably. \n \nRates of psychological distress were substantially higher in households who started using foodbanks in the pandemic (53% among young people and 63% among parents), compared to 41% and 33% for those not using foodbanks. They were also slightly higher than ‘long term’ users, potentially indicating the impacts of short-term financial shocks.
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 | 0,004 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,024 | 0,018 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,080 | 0,018 |
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 ».