Like Mother, Like Daughter: Intergenerational Programs for Hispanic Girls

Hispanic mothers\u27 involvement with and expectations of their children coupled with their position as role-models, have a lasting impact on their children\u27s academic achievement

Hvordan relaterer seg motivasjon og mestring til fravær? : En oppfølgingsstudie av ungdommer i studie og arbeidsliv

Bakgrunn: Fravær fra arbeid har alvorlige konsekvenser for både individ og samfunn. Fraværet blant ungdom viser for tiden en fallende tendens, men undersøkelser viser at unges bruk av trygdeordninger øker. Dette kan skyldes et økende misforhold mellom tilpasningskrav i arbeidslivet og mestringsressurser hos de unge. Dette utgjør et økende samfunnsproblem og det er derfor viktig å få frem kunnskap og identifisere individuelle faktorer og sammenhenger som kan virke inn på unge arbeidstakeres fravær. Formålet med denne studien var å undersøke mulige assosiasjoner mellom unge voksnes fravær og deres mestring, mestringsforventning (self-efficacy) og motivasjon. I tillegg ble faktorer som helseplager og fysiske og psykososiale krav i arbeidet inkludert i studien. Materiale og metode: Studien er en del av en større prospektiv studie om Mulige Årsaker til Muskel og Skjelettplager (MÅMS) ved Statens Arbeidsmiljøinstitutt (STAMI). Utvalget for analyse i denne masteroppgaven består av 92 deltakere. De ble fulgt fra sluttet av lærlingperioden og de første årene i arbeidslivet. Selvrapportert informasjon om fravær og mestring, motivasjon og helse samt psykososiale faktorer og mekanisk eksponering i arbeidslivet ble samlet inn og analysert i krysstabeller, korrelasjonsanalyser og med multivariat logistisk regresjon. Resultater og diskusjon: 58 prosent, av utvalget hadde fravær mindre en 2 dager per kvartal i en treårsperiode noe som tilsvarer maksimum 7 dager per år. De med høyere fravær rapporterte høy grad av mestringsevne. Resultatet var overraskende og viste at høy grad av mestring var relatert til høyt fravær. Dette ser ut til å være sterkest for gutter. Dette ble tolket som at ungdom kan bruke fravær som et aktiv valg eller som en mestringsstrategi for å kontrollere situasjonen de er i. Funnene viser også at det var mye høyere risiko for fravær ved psykiske plager. Det kan også se ut som mestring har hatt en modererende effekt på variabelen indre motivasjon. Smerte er tradisjonelt assosiert med fravær, men det er ikke funnet sikre holdepunkter til dette i denne studien. Konklusjon: Selvrapportert høy grad av mestring i forhold til arbeid hadde sammenheng med høyt fraværsnivå. Dette kan tolkes som at fravær brukes som et aktivt valg eller som en mestringsstrategi. Det var mye høyere risiko for fravær ved psykiske plager. Det ser ikke ut som motivasjon eller smerte har direkte betydning for fravær i denne studien. Mer forskning er nødvendig for å kunne forklare årsaker til unge arbeidstakeres fravær og identifisere faktorer som er assosiert med dette. Stikkord: Fravær, mestring, self-efficacy, indre og ytre motivasjon, unge arbeidstaker

Why do some cities adopt more diversity policies than others?:A study in France and Germany

An increasing sociocultural heterogeneity of populations and vocal demands for the recognition of diversity have become common features of, in particular, cities in Western Europe. Do cities reshape policies in response to such developments? And to what extent do they implement policies that accommodate difference? We use data from an original survey of urban policy actors in the twenty largest cities of France and Germany to identify city-level diversity policy instruments. In both countries, such instruments are widespread, contradicting assumptions of dominant assimilationist paradigms. And yet, the degree of adoption across cities varies. Drawing on institutionalist theory, we investigate what might explain differing adoption rates. The main finding is that key determinants at the urban level differ between the two countries. In France, the political constellation is crucial; higher numbers of diversity policies are associated with centre-left dominance. In contrast, in German cities, political consensus around diversity policies seems to prevail and higher adoption rates are associated with higher population diversity. Our findings provide a first wide-ranging account of the adoption of diversity policy instruments in European cities. They demonstrate that such policies exist at a relevant scale. They further help explain why the adoption of diversity policy instruments is uneven

Nutzung gewalthaltiger Bildschirmspiele als längsschnittlicher Risikofaktor für Cyberbullying in der frühen Adoleszenz

"Die Studie untersucht mittels Cross-Lagged-Panel-Design längsschnittliche Zusammenhänge zwischen gewalthaltiger Bildschirmspielnutzung und Cyberbullying. Zur Erklärung dieser Zusammenhänge werden die Selektions- und die Sozialisationshypothese überprüft. Traditionelles Bullying, Viktimisierung, offene und relationale Aggression werden als mögliche Kovariaten von Cyberbullying mitberücksichtigt. Im Zeitabstand eines Jahres wurden Selbsteinschätzungen von 271 Jugendlichen (10-13 Jahre) zu zwei Messzeitpunkten erhoben. Mittels Strukturgleichungsmodellen konnte gezeigt werden, dass die Nutzung gewalthaltiger Bildschirmspiele ein Risikofaktor für Cyberbullying, traditionelles Bullying und offene Aggression (Sozialisationseffekt) ist. Zudem ist traditionelles Bullying ein Risikofaktor für gewalthaltige Bildschirmspielnutzung (Selektionseffekt)." (Autorenreferat)"This study investigates longitudinal associations between violent video game playing und cyberbullying using a Cross-Lagged-Panel Design. The selection and the socialization hypothesis were tested as possible explanations. Traditional bullying, victimization, overt and relational aggression were considered as covariates in the analyses. Within one year, self-reports were collected from 271 adolescents, aged between 10 and 13 years, at two measurement occasions. Structural-Equation-Models identified violent video game playing as a longitudinal risk factor for cyberbullying, traditional bullying and overt aggression (socialization effect). Furthermore, traditional bullying was found as a longitudinal risk factor for violent video game playing (selection effect)." (author's abstract

Simulating high-pressure surface reactions with molecular beams

Using a reactive molecular beam with high kinetic energy ($E_{kin}$) it is possible to speed gas-surface reactions involving high activation barriers ($E_{act}$), which would require elevated pressures ($P_0$) if a random gas with a Maxwell-Boltzmann distribution is used. By simply computing the number of molecules that overcome the activation barrier in a random gas at $P_0$ and in a molecular beam at $E_{kin}$=$E_{act}$, we establish an $E_{kin}$-$P_0$ equivalence curve, through which we postulate that molecular beams are ideal tools to investigate gas-surface reactions that involve high activation energies. In particular, we foresee the use of molecular beams to simulate gas surface reactions within the industrial-range ($>$ 10 bar) using surface-sensitive Ultra-High Vacuum (UHV) techniques, such as X-ray photoemission spectroscopy (XPS). To test this idea, we revisit the oxidation of the Cu(111) surface combining O$_2$ molecular beams and XPS experiments. By tuning the kinetic energy of the O$_2$ beam in the range 0.24-1 eV we achieve the same sequence of surface oxides obtained in Ambient Pressure Photoemission (AP-XPS) experiments, in which the Cu(111) surface was exposed to a random O$_2$ gas up to 1 mbar. We observe the same surface oxidation kinetics as in the random gas, but with a much lower dose, close to the expected value derived from the equivalence curve

“Vielfalt and diversité: how local actors in France and Germany evaluate immigration and socio-cultural heterogeneity”

In both Germany and France, perceptions of immigration, diversity and their societal consequences have undergone important transformations in the past two decades. However, existing research has o

Generalized Sparse Convolutional Neural Networks for Semantic Segmentation of Point Clouds Derived from Tri-Stereo Satellite Imagery

We studied the applicability of point clouds derived from tri-stereo satellite imagery for semantic segmentation for generalized sparse convolutional neural networks by the example of an Austrian study area. We examined, in particular, if the distorted geometric information, in addition to color, influences the performance of segmenting clutter, roads, buildings, trees, and vehicles. In this regard, we trained a fully convolutional neural network that uses generalized sparse convolution one time solely on 3D geometric information (i.e., 3D point cloud derived by dense image matching), and twice on 3D geometric as well as color information. In the first experiment, we did not use class weights, whereas in the second we did. We compared the results with a fully convolutional neural network that was trained on a 2D orthophoto, and a decision tree that was once trained on hand-crafted 3D geometric features, and once trained on hand-crafted 3D geometric as well as color features. The decision tree using hand-crafted features has been successfully applied to aerial laser scanning data in the literature. Hence, we compared our main interest of study, a representation learning technique, with another representation learning technique, and a non-representation learning technique. Our study area is located in Waldviertel, a region in Lower Austria. The territory is a hilly region covered mainly by forests, agriculture, and grasslands. Our classes of interest are heavily unbalanced. However, we did not use any data augmentation techniques to counter overfitting. For our study area, we reported that geometric and color information only improves the performance of the Generalized Sparse Convolutional Neural Network (GSCNN) on the dominant class, which leads to a higher overall performance in our case. We also found that training the network with median class weighting partially reverts the effects of adding color. The network also started to learn the classes with lower occurrences. The fully convolutional neural network that was trained on the 2D orthophoto generally outperforms the other two with a kappa score of over 90% and an average per class accuracy of 61%. However, the decision tree trained on colors and hand-crafted geometric features has a 2% higher accuracy for roads