Australian Queer Science Fiction Fans

Science fiction (sf) does more than provide a fleeting moment of entertainment; it has many personal and social functions. In addition to offering audiences “romantic escapism” (Gerrold, 1996, pp. 5–6), sf also enables the “postulation of an alternative reality from which to contemplate this one” (Gerrold, 1996, pp. 5–6); as such, it is especially important “for groups which have had limited stakes in the status quo” (Jenkins, 1995, p. 242). To date, no research has been undertaken on the relationship between Australian queers and sf fandom. This article reports the findings of an online survey and explores the psycho-social features of Australian queer sf fans and why they like the genre. While the characteristics of this sample mirror those of Australian queers generally, they also have slightly higher rates of mental illness and are far more likely to state they have “no religion.” Furthermore, while enjoying the “sciency” (P10, bisexual woman) aspects of sf, Australian queers also like the “poignant metaphors for our own civilization” (P45, asexual man)     

The Effects of Middle School Weight Climate on Youth With Higher Body Weight

This study examines whether social‐emotional difficulties associated with higher body weight vary across schools as a function of the school's weight climate. Weight climate, characterized by weight‐policing, was assessed indirectly by examining how strongly self‐reported weight predicts victim reputation within 26 ethnically diverse middle schools. Social‐emotional indicators included self‐reported loneliness, school belonging, and self‐esteem. In schools with stronger weight‐policing at seventh grade, loneliness was intensified by eighth grade among both girls (n = 2,101) and boys (n = 1,985) with higher weight. Similar effects were found for low self‐esteem among girls. Additionally, boys—regardless of their weight—reported lower sense of belonging in schools with stronger weight‐policing. The study offers a new method to estimate school weight climate, and the findings provide insights for interventions.     

An Experimental Comparison of Time-Based and Economic-Based Scheduling Methods to Maximize Net Present Value*

In the past, performance in dynamic-scheduling environments was primarily measured in terms of time or physical shop characteristics. Objectives such as mean tardiness, flow time, and work-in-process inventory were commonly used. Today, there is increasing interest in the use of more advanced economic performance measures. These measures have the more comprehensive objective of maximizing ownership wealth by economically scheduling jobs and tasks. This study presents a large-scale experiment testing time-based and economic-based scheduling methods in a dynamic job shop. These methods are evaluated on their ability to maximize net present value (NPV). The study considers the just-in-time (JIT) delivery environment. The job shop is hypothetical, but is based on models of real production situations. Results show that the use of very detailed economic information in a sophisticated manner generally improves economic performance. Where due dates are easy to achieve, however, time-based scheduling methods are at least as good as those based on economics. Also, where utilization is high and due dates tight, early cost information in release and dispatch is detrimental to schedule value.     

An Application of the AXIS Solution Framework to Multiple Objective Aggregate Production Planning

This paper provides data on the first application of a prototype of the AXIS solution framework. AXIS (algorithms combined with knowledge systems in an interactive sequence) is a framework for interactively combining structured algorithms that seek a best solution with knowledge-based expert systems that seek expert heuristic solutions. This paper tests the framework using an interactive multiple objective integer programming algorithm combined with heuristics taken from the domain of aggregate production planning. The results indicate the AXIS framework can be successful in generating high quality solutions, in vastly reduced solution times compared to the structured algorithms, at much lower costs compared to the expert heuristics working alone.     

Solving Resource Allocation Problems When Earning Effects Are Present

Learning effects play an important role in certain resource allocation problems, and several authors have proposed models for these problems that capture the relevant relationships. However, the models may be difficult to implement or have shortcomings in the prescribed solution procedures. In this paper, we selectively review the work to date and present a simple reformulation that facilitates solution by off-the-shelf software.     

ASSESSMENT OF MULTIATTRIBUTED MEASURABLE VALUE AND UTILITY FUNCTIONS VIA MATHEMATICAL PROGRAMMING*

A major restriction on the use of decision analysis in practice is the frequent difficulty of determining a decision maker's multiattribute utility function. The assessment process can be complex and tedious and generally involves: (1) identifying relevant independence conditions, (2) assessing conditional utility functions, (3) assessing scaling constants, and (4) checking for consistency. Some of the assessment and modeling complexities encountered include an assessor's inability to respond in a quantitatively meaningful and consistent way to hypothetical gambles and an analyst's problem in selecting an appropriate functional form that accurately characterizes the conditional utility assessments. A simplified procedure that mitigates these difficulties is proposed. This procedure facilitates the determination of scaling constants by obtaining (via mathematical programming) a multiattributed measurable value function which is converted to a multiattributed utility function. The methodology can be developed advantageously to produce an interactive software package for use as an assessment aid.     

Institutional Determinants of Collaboration: An Empirical Study of County Open-Space Protection

Collaboration is an increasingly important topic in the publicadministration and management literatures. A preponderance ofstudies focuses on how managers can build trust between thegovernment and collaborative partners by means of behavioralattributes and managerial skill. In this article, the authorsuggests that stable institutions and local government structurefacilitate collaboration by allowing public managers to morecredibly commit in a policy arena. Using county data on open-spacepolicy, the author finds empirical support for the propositionthat county form of government, along with rules governing debtaccumulation and administrative commitment, increases the breadthof county collaboration in open-space protection.     

Abstract Rule Learning for Visual Sequences in 8‐ and 11‐Month‐Olds

The experiments reported here investigated the development of a fundamental component of cognition: to recognize and generalize abstract relations. Infants were presented with simple rule‐governed patterned sequences of visual shapes (ABB, AAB, and ABA) that could be discriminated from differences in the position of the repeated element (late, early, or nonadjacent, respectively). Eight‐month‐olds were found to distinguish patterns on the basis of the repetition, but appeared insensitive to its position in the sequence; 11‐month‐olds distinguished patterns over the position of the repetition, but appeared insensitive to the nonadjacent repetition. These results suggest that abstract pattern detection may develop incrementally in a process of constructing complex relations from more primitive components.     

Application of the Net Present Value Criterion in Random and Flow Shop Scheduling*

While the majority of the literature on shop scheduling has emphasized time-based performance criteria such as mean flow time, lateness, and tardiness, the primary goal of management should be the maximization of shop profitability. In this research the net present value (NPV) criterion is introduced to measure shop profitability. This measure combines aspects of job flow time and inventory holding costs into a single measure. A simulation model of a job shop is used to examine the performance of a variety of time- and value-based scheduling rules. These rules are evaluated with respect to the NPV criterion in both random and flow shop environments. The results suggest that priority rules that utilize monetary information about jobs yield a higher NPV than many time-based rules in most situations, with little sacrifice in job tardiness. A well-researched time-based rule, critical ratio, also provides excellent performance when the shop is heavily loaded.     

The impact of missing data and how it is handled on the rate of false-positive results in drug development

In drug development, a common choice for the primary analysis is to assess mean changes via analysis of (co)variance with missing data imputed by carrying the last or baseline observations forward (LOCF, BOCF). These approaches assume that data are missing completely at random (MCAR). Multiple imputation (MI) and likelihood-based repeated measures (MMRM) are less restrictive as they assume data are missing at random (MAR). Nevertheless, LOCF and BOCF remain popular, perhaps because it is thought that the bias in these methods lead to protection against falsely concluding that a drug is more effective than the control. We conducted a simulation study that compared the rate of false positive results or regulatory risk error (RRE) from BOCF, LOCF, MI, and MMRM in 32 scenarios that were generated from a 2(5) full factorial arrangement with data missing due to a missing not at random (MNAR) mechanism. Both BOCF and LOCF inflated RRE were compared to MI and MMRM. In 12 of the 32 scenarios, BOCF yielded inflated RRE compared with eight scenarios for LOCF, three scenarios for MI and four scenarios for MMRM. In no situation did BOCF or LOCF provide adequate control of RRE when MI and MMRM did not. Both MI and MMRM are better choices than either BOCF or LOCF for the primary analysis.