A horse named Adolf: European-Jewish humor during the Holocaust

This thesis examines an understudied area of Holocaust research -- Jewish humor. It argues that jokes provide a valuable and representative look at trends in political and religious thought in Jewish communities during this very difficult time. A statistical analysis of jokes was conducted to determine the links between several characteristics of jokes, such as joke topic and time period. The implications of Jewish humor (particularly anti-Nazi humor) can be seen in the anti-establishment humor in post-war Communist nations and in minority humor in present-day societies. Additionally, Jewish humor as a coping mechanism can inform how the psychological community regards humor in the aftermath of events like the September 11, 2001 terrorist attacks

Characteristics of included patients, United Kingdom, 2003–2014.

<p>Characteristics of included patients, United Kingdom, 2003–2014.</p

Table_1_‘Kindness by Post’: A Mixed-Methods Evaluation of a Participatory Public Mental Health Project.DOCX

BackgroundRandom acts of kindness can improve wellbeing. However, less is known about the impacts of giving and receiving acts of kindness with strangers on wellbeing and loneliness. Therefore, this study’s objectives were to evaluate a participatory public mental health project involving sending and receiving a card with goodwill messages, to understand how such acts of kindness influence wellbeing and loneliness, and to investigate the potential mechanisms underlying the project’s impacts.Materials and MethodsThis study was an analysis of anonymized service evaluation data collected in the ‘Kindness by Post’ (KBP) project in 2020. It used a mixed-methods single-group design and data from 289 participants. Changes in wellbeing, loneliness, sense of belonging and hope from baseline (12th–14th February) to follow-up (26th February–2nd March) were analyzed using linear or multinomial logistic regression. Regression models also examined the associations between changes in wellbeing and baseline loneliness or participation level. Free text responses about experiences and suggestions for the project were analyzed using thematic analysis.ResultsParticipants had a small, but statistically significant improvement, in wellbeing equating to 0.21 standard deviations (95% CI: 0.12–0.30) after taking part in the project, as well as improvements in loneliness, sense of belonging and hope. How lonely a participant was at baseline and whether participants both sent and received a kindness card were not associated with improvements in wellbeing. In the qualitative analysis, a desire to help others emerged as the main motivator to take part in the card exchange. Participants reported enhanced personal fulfillment, leading to improvements in wellbeing. Receiving a card could make people feel special and cherished, which was reported to establish a sense of connection with others, with potential benefits for reducing loneliness.ConclusionThis study provided preliminary evidence that the KBP project might improve wellbeing, loneliness, sense of belonging and hope. Sending a kindness card in this project played a predominant role in wellbeing enhancement, and receiving a kindness card could reduce loneliness. This study suggests that the KBP project can be replicated in more contexts in the future, and might improve wellbeing and loneliness in large communities.</p

The study design and data scenarios.

The study design and data scenarios.</p

Incidence rate ratios (IRR) for patient death by risk period.

<p>Incidence rate ratios (IRR) for patient death by risk period.</p

Incidence rate ratios from a self-controlled case series analysis for patient death during the 24 months after cohabitee death.

<p>Incidence rate ratios from a self-controlled case series analysis for patient death during the 24 months after cohabitee death.</p

Histograms of the eigenvector centrality (EC), closeness centrality (CC), and betweenness centrality (BC) for the baseball and basketball-Twitter networks shown left, center, and right, respectively.

Baseball is shown in orange and basketball is shown in blue in each histogram.</p

Supplementary material for using social networks to improve team transition prediction in professional sports.

The supplementary material includes informative data that extends the data presented in the main body of the work. It includes a complete summary information for all of the machine learning algorithms utilized, and all of the combinations of features. It also includes the tables of the most socially active players in both baseball and basketball for all of the centralities we consider. (PDF)</p

Baseball inference accuracy: The inference accuracy using XGBoost for team transition in MLB during the time periods 2002–2010, 2011–2019, and 2002–2019 is shown.

Baseball inference accuracy: The inference accuracy using XGBoost for team transition in MLB during the time periods 2002–2010, 2011–2019, and 2002–2019 is shown.</p

Three years of collected and feature engineered data for a random baseball player.

We observe that at the conclusion of the 2017 season, this player transitioned from the Miami Marlins (MIA) to the New York Yankees (NYY). Thus in 2017 his target value is set to NYY. In this table, and in Table 3 we use the abbreviation CL for career length (an engineered variable).</p