Does Decertification Work? Outcome Analysis of the National Football Leagues Negotiated Order (1986-2008).

For decades, union membership and activity has been declining in North America; employers have demanded greater flexibility and have successfully weakened workplace and worker protections. Modern workers increasingly use alternative strategies to negotiate conditions of employment with managers who have limited their discretionary power. Negotiated order theory provides a useful tool for analyzing the mesostructural arrangements of bargaining parties during labor disputes. This thesis applies negotiated order theory to explore how and why the National Football League (NFL) players have twice decertified their union and sought court intervention to challenge the legitimacy of the League\u27s highly restrictive reserve system. An outcome-focused content analysis was designed as a preliminary investigation to ascertain why an alternative strategy was sought and if the strategy proved more effective in securing the players\u27 preferred ends than conventional collective bargaining. The NFL case offers a fixed market from which to formulate a negotiation context of the interorganizational structures and bargaining interactions of its members

Variability of Persistent Temporal Correlation in Climate Data

This dissertation examines manifestations of persistent memory in climate data. Persistence is characterized by a slow power-law decay in the autocorrelations of a time series. Its existence implies that the influence of past values in a time series extend into the distant future. It has numerous theoretical implications, notably that it changes the asymptotic decay in the variance of sample means, which can substantially impact the uncertainty in climate mean states. Its intensity can vary over space, time, and other dimensions, e.g. tree species. Variation in its intensity can be used for practical applications such as discriminating between steady and intermittent rainfall and assessing the calibration period needed for paleoclimate proxy data. This work explores three major areas in which persistence can be leveraged to better understand the complexities of climate data. The first is in tree ring width data, which are among the best proxies for reconstructing paleoclimate records. The persistent correlations found in tree ring data suggest that the behavior of tree ring growth observed in a short calibration period may be similar to the general behavior of tree ring growth in a much longer period; therefore, the limited calibration period may be more useful than previously thought. The second area is in the quantification of uncertainty in the mean states of climate data. A framework for quantifying uncertainty in climate means is presented which can account for both classical short-range correlations and long-term persistent correlations. The final area is in the detection of subtle changes in tropical rainfall patterns. Persistence is used to illuminate recent changes in the temporal clustering patterns of rainfall in the tropical belt; the detected changes could have critical implications for the water resource management of the affected regions

Does recreational computer use affect high school achievement?

Historically, the relationship between student academic achievement and use of computers for fun and video gaming has been described from a multitude of perspectives, from positive, to negative, to neutral. However, recent research has indicated that computer use and video gaming may be positively associated with achievement, yet these studies have focused on small intact and qualitative samples. The purpose of the present study is to examine the association between academic achievement in high school and student use of computers for fun and video gaming using the large nationally representative ELS:2002 sample of students in grade 10 in 2002 and an independent effects two-level hierarchical linear model. Our results indicate that both student use of computers for fun and moderate levels of video gaming were positive and significant on cross-sectional reading and mathematics achievement assessments in high school, controlling for multiple covariates of achievement, but were not related to growth in mathematics from grade 10 to grade 12

Modelling bluetongue virus transmission between farms using animal and vector movements.

Bluetongue is a notifiable disease of ruminants which, in 2007, occurred for the first time in England. We present the first model for bluetongue that explicitly incorporates farm to farm movements of the two main hosts, as well as vector dispersal. The model also includes a seasonal vector to host ratio and dynamic restriction zones that evolve as infection is detected. Batch movements of sheep were included by modelling degree of mixing at markets. We investigate the transmission of bluetongue virus between farms in eastern England (the focus of the outbreak). Results indicate that most parameters affecting outbreak size relate to vectors and that the infection generally cannot be maintained without between-herd vector transmission. Movement restrictions are effective at reducing outbreak size, and a targeted approach would be as effective as a total movement ban. The model framework is flexible and can be adapted to other vector-borne diseases of livestock

Two-Host, Two-Vector Basic Reproduction Ratio (R-0) for Bluetongue

Mathematical formulations for the basic reproduction ratio (R (0)) exist for several vector-borne diseases. Generally, these are based on models of one-host, one-vector systems or two-host, one-vector systems. For many vector borne diseases, however, two or more vector species often co-occur and, therefore, there is a need for more complex formulations. Here we derive a two-host, two-vector formulation for the R (0) of bluetongue, a vector-borne infection of ruminants that can have serious economic consequences; since 1998 for example, it has led to the deaths of well over 1 million sheep in Europe alone. We illustrate our results by considering the situation in South Africa, where there are two major hosts (sheep, cattle) and two vector species with differing ecologies and competencies as vectors, for which good data exist. We investigate the effects on R (0) of differences in vector abundance, vector competence and vector host preference between vector species. Our results indicate that R (0) can be underestimated if we assume that there is only one vector transmitting the infection (when there are in fact two or more) and/or vector host preferences are overlooked (unless the preferred host is less beneficial or more abundant). The two-host, one-vector formula provides a good approximation when the level of cross-infection between vector species is very small. As this approaches the level of intraspecies infection, a combination of the two-host, one-vector R (0) for each vector species becomes a better estimate. Otherwise, particularly when the level of cross-infection is high, the two-host, two-vector formula is required for accurate estimation of R (0). Our results are equally relevant to Europe, where at least two vector species, which co-occur in parts of the south, have been implicated in the recent epizootic of bluetongue

Language Models Can Teach Themselves to Program Better

Recent Language Models (LMs) achieve breakthrough performance in code generation when trained on human-authored problems, even solving some competitive-programming problems. Self-play has proven useful in games such as Go, and thus it is natural to ask whether LMs can generate their own instructive programming problems to improve their performance. We show that it is possible for an LM to synthesize programming problems and solutions, which are filtered for correctness by a Python interpreter. The LM's performance is then seen to improve when it is fine-tuned on its own synthetic problems and verified solutions; thus the model 'improves itself' using the Python interpreter. Problems are specified formally as programming puzzles [Schuster et al., 2021], a code-based problem format where solutions can easily be verified for correctness by execution. In experiments on publicly-available LMs, test accuracy more than doubles. This work demonstrates the potential for code LMs, with an interpreter, to generate instructive problems and improve their own performance.Comment: 22 pages, 14 figure

Knowing What Matters: An Expanded Study of School Bond Elections in Michigan, 1998-2006

This study investigates what factors are associated with the likelihood of passing school facility construction bonds by local district election. It uses statewide data from Michigan, 1998–2006, to examine the outcome of 789 bond elections in terms of ten variables: amount of the bond request; district enrollment; district locale; percentage of students receiving free school lunches; percentage of the district population with only a high school degree; the district’s long-term debt; voter turnout; the day of the calendar year on which the election is held; the number of the bond proposal on the ballot; and the inclusion of technology in the ballot proposal’s wording. The logistic regression analysis finds that bond amount, percentage of students receiving free lunches, percentage of district population with only a high school degree, voter turnout, and being further down on the ballot are all negative and significant factors. District long-term debt and holding the election later in the calendar year are both positive and significant factors. District enrollment numbers are non-significant. In terms of district locale (using mid-sized city/suburban districts as the reference group), being a small town and rural district is a negative and significant factor

Why José Mourinho’s protégés failed when they became managers

Martin Kilduff, Craig Crossland, Wenpin Tsai and Matthew T. Bowers discuss the acolyte effec