The Educational and Employment Aspirations of Adolescents from Areas of High Deprivation in London

Adolescents from areas of high deprivation are often assumed to have low aspirations for the future. However, recent research has suggested otherwise and there have been calls for more substantial investigation into the relationship between poverty and aspiration. This article reports levels and variation in aspiration from 1,214 adolescents (49.5% male; 50.5% female) living in areas of high deprivation across 20 London boroughs. A strength of this study is our large and diverse population of low socio-economic status (SES) adolescents, comprising of white British (22%), black African (21%), black Caribbean (9%), Indian/Pakistani/Bangladeshi/Other Asian (24%), mixed ethnicity (9%), and 15% defining themselves as Other. Our measures indicated a high group level of reported aspiration with notable variations. Females reported higher educational (but not occupational) aspirations than males; white British students reported lower educational and occupational aspirations than other ethnic groups; and black African children reported the highest educational aspirations. Perceived parental support for education had the largest positive association with aspirations. In contrast to previous findings from studies carried out in the United States, aspirations were found to be negatively associated with perceptions of school and school peer environment. These measures explored feelings of safety, happiness and belonging within the school environment and school peer group. We discuss possible explanations for this unexpected finding within our population of adolescents from UK state schools and how it might affect future policy interventions. This study makes an important contribution to the literature on adolescent aspirations because of the unique nature of the data sample and the multiple domains of functioning and aspiration measured.

HG 362.70-362.80

Multiple domains are present. Clasts range from small to large in size and sub-angular to sub-rounded in shape. The fine grained domain is dark brown with some lineations and organic material. The coarse grained domain is grey in colour and contains mainly lineations and rotation structures. Some comet structures can also be seen.

HG 484.65-484.80

Brown sediment with multiple domains. Grains range from small to large in size, and angular to sub-rounded in shape. Large amounts of grain crushing can be seen in the coarser domains. The sample is mainly dominated with grain crushing. The finer grained domains contain some clay rich material. Some lineations and rotation structures can also be seen throughout the different domains.

HG 129.50-129.60

Brown sediment with multiple domains. There are two finer grained domains that contain a few small clasts. These two can be distinguished based on the abundance of clay material(darker brown). The coarse grained sediment contains clasts ranging from small to medium in size, and angular to sub-rounded in shape. This domain contains lineations, rotation structures, comet structures, and some edge-to-edge grain crushing.

HG 129.65-129.75

Brown sediment with two main domains; a fine grained one rich in organic material and a coarse grained domain. The fine grained domain appears in several areas of the sample, and in one area alternates with the coarser domain. The coarse grained domain contains clasts ranging from small to medium in size, and angular to sub-rounded in shape. Grain crushing and lineations can commonly be seen in this domain.

HG 168.50-168.60

Brown sediment with three main domains; two different fine grained domains and one coarse grained domain. The fine grained domains can be distinguished based on the abundance of organic material (darker). Both domains contain lineations. The coarse grained domain contains clasts ranging from small to medium in size and angular to rounded in shape. Rotation structures, lineations and comet structures can be seen in this domain. It also contains some fractured grains.

HG 174.30-174.45

Brown sediment with two different domains; one coarse grained, and one finer grained. In the coarse grained domain, clasts range from small to large in size. The clast shape ranges from sub-angular to rounded. Rotation structures and edge-to-edge grain crushing is commonly seen. In the finer grained domain, clasts are mainly small, with a few larger clasts, and there is an abundance of lineations.

PBURWELL #1-87

This sample contains two main domains. One is a light brown sediment domain with mainly small clasts which are clustered together. Only a few larger clasts can be seen in this domain. The other one is dark brown with well dispersed clasts. The light brown domain contains clasts that range from sub-angular to sub-rounded in shape. Lineations can be commonly seen in this domain. Grain crushing is also common with minor amounts of rotation. In the dark brown domain, clasts range from small to medium in size. They range from sub-angular to sub-rounded in shape. A few lineations and rotation structures can be seen in this domain.

PBURWELL #4-87

Brown sediment with clasts ranging from small to medium in size. Clast shape ranges from sub-angular to rounded. Two mains domains can be observed; one mainly contains small clasts, and the other is a mix of small and medium sized clasts. The finer grained domain contains some organic material and is abundant in lineations. The coarser domain is also abundant in lineations but also contains crushed grains and comet structures.

8AUG 6-D

Dark grey ground mass, with one other lighter domain. Both domains are extremely fine grained.

9 AUG B-2-B

This sample contains multiple domains. One domain is a fine grained greyish-brown ground mass, with some small clasts. Another domain is a dark grey, structure-less ground mass. Another domain is a light greyish-brown domain that is generally fine grained with clay inclusions.

WR-2

Dark brown sediment with clasts ranging from small to large in size. Clast shape ranges from angular to sub-rounded. The main domain mainly contains larger aggregates. There is one domain inclusion in this sample. It mainly contains small and medium sized clasts, and contains many lineations. Necking structures can be commonly seen in the main domain between larger aggregates. This sample also contains many elongated clasts and inclusions of clay material.

1520 BH17 NP 2014

Dark brown sediment with clasts ranging from small to large in size. Clast shape ranges from angular to sub-rounded. Lineations can be seen throughout the sample, along with a few rotation and comet structures. This sample also contains a fine grained clay domain that is relatively structure-less. It can be seen scattered throughout the sample.

1539 BH17 NP 2014

Brown sediment with clasts ranging from small to large. Clast shape ranges from angular to sub-rounded. Lineations are common throughout the sample. This sample also contains a clay domain, that appears very fine grained. Edge-to-edge grain crushing, comet structures, and rotation structures are also present.

Apprentissage d'espaces sémantiques

Dans cette dissertation, nous présentons plusieurs techniques d’apprentissage d’espaces sémantiques pour plusieurs domaines, par exemple des mots et des images, mais aussi à l’intersection de différents domaines. Un espace de représentation est appelé sémantique si des entités jugées similaires par un être humain, ont leur similarité préservée dans cet espace. La première publication présente un enchaînement de méthodes d’apprentissage incluant plusieurs techniques d’apprentissage non supervisé qui nous a permis de remporter la compétition “Unsupervised and Transfer Learning Challenge” en 2011. Le deuxième article présente une manière d’extraire de l’information à partir d’un contexte structuré (177 détecteurs d’objets à différentes positions et échelles). On montrera que l’utilisation de la structure des données combinée à un apprentissage non supervisé permet de réduire la dimensionnalité de 97% tout en améliorant les performances de reconnaissance de scènes de +5% à +11% selon l’ensemble de données. Dans le troisième travail, on s’intéresse à la structure apprise par les réseaux de neurones profonds utilisés dans les deux précédentes publications. Plusieurs hypothèses sont présentées et testées expérimentalement montrant que l’espace appris a de meilleures propriétés de mixage (facilitant l’exploration de différentes classes durant le processus d’échantillonnage). Pour la quatrième publication, on s’intéresse à résoudre un problème d’analyse syntaxique et sémantique avec des réseaux de neurones récurrents appris sur des fenêtres de contexte de mots. Dans notre cinquième travail, nous proposons une façon d’effectuer de la recherche d’image ”augmentée” en apprenant un espace sémantique joint où une recherche d’image contenant un objet retournerait aussi des images des parties de l’objet, par exemple une recherche retournant des images de ”voiture” retournerait aussi des images de ”pare-brises”, ”coffres”, ”roues” en plus des images initiales.