NAVIGATING THE PATH TO PRESENCE: IDEOLOGY, POLITICS, AND THE CAMPAIGN FOR GENDER BALANCED BOARDS AND COMMISSIONS IN IOWA

From 1986 through 1988, Iowa adopted and strengthened a gender balance law that required men and women be equally represented on state boards and commissions. In 2009, Iowa extended this law to also require its counties, municipalities, and school districts to gender balance their boards and commissions. Iowa’s law remains unique in the United States. Through archival research and interviews, my research investigates how advocates navigated the ideological landscape associated with this policy issue. My research unveils the mechanisms that substantially deradicalized gender balance in Iowa, enabling its passage and shifting Iowans’ perceptions of gender, governance, and affirmative action—disembedding gender segregation, normatizing and institutionalizing gendered representation practices, and prioritizing an ideology of good governance. Based on my findings and analyses, I argue for reconceptualizing ideology through navigation theory—actors simultaneously hold multiple complementary and competing ideologies and must negotiate how these ideologies are (de)activated, (de)prioritized, and interpreted and applied to the issue under consideration. In Iowa, advocates employed collective action frame management to facilitate and steer this navigation such that a majority of legislators voted for and governors signed these affirmative action legislation

Navigating the Path to Presence: Ideology, Politics, and the Campaign for Gender Balanced Boards and Commissions in Iowa

From 1986 through 1988, Iowa adopted and strengthened a gender balance law that required men and women be equally represented on state boards and commissions. In 2009, Iowa extended this law to also require its counties, municipalities, and school districts to gender balance their boards and commissions. Iowa’s law remains unique in the United States. Through archival research and interviews, my research investigates how advocates navigated the ideological landscape associated with this policy issue. My research unveils the mechanisms that substantially deradicalized gender balance in Iowa, enabling its passage and shifting Iowans’ perceptions of gender, governance, and affirmative action—disembedding gender segregation, normatizing and institutionalizing gendered representation practices, and prioritizing an ideology of good governance. Based on my findings and analyses, I argue for reconceptualizing ideology through navigation theory—actors simultaneously hold multiple complementary and competing ideologies and must negotiate how these ideologies are (de)activated, (de)prioritized, and interpreted and applied to the issue under consideration. In Iowa, advocates employed collective action frame management to facilitate and steer this navigation such that a majority of legislators voted for and governors signed these affirmative action legislation

Disordering mathematics, citizenship and socio-political research in mathematics education amongst the “rubble of words”

In this contribution, we seek to problematise not just mathematics and (global) citizenship but also the process of researching with a critical intent. We argue for a disorderly approach and use researching our participation in a European funded project – the Project in Citizenship and Mathematics (PiCaM) – as an illustration of the complexities and contradictions involved. Despite the need for inconclusiveness and an awareness of how language is colonised, we argue for action and hope

Probabilistic photometric redshift estimation in massive digital sky surveys via machine learning

The problem of photometric redshift estimation is a major subject in astronomy, since the need of estimating distances for a huge number of sources, as required by the data deluge of the recent years. The ability to estimate redshifts through spectroscopy does not scale with this avalanche of data. Photometric redshifts provide the required redshift estimates at the cost of some precision. The success of several forthcoming missions is highly dependent on the availability of photometric redshifts. The purpose of this thesis is to provide innovative methods for photometric redshift estimation. Two models are proposed. The first is fully-automatized, based on the combination of a convolutional neural network with a mixture density network, to predict probabilistic multimodal redshifts directly from images. The second model is features-based, performing a massive combination of photometric parameters to apply a forward selection in a huge feature space. The proposed models perform very efficiently compared to some of the most common models used in the literature. An important part of the work is dedicated to the correct estimation of the errors and prediction quality. The proposed models are very general and can be applied to different topics in astronomy and beyond

In Search of a Common Thread: Enhancing the LBD Workflow with a view to its Widespread Applicability

Literature-Based Discovery (LBD) research focuses on discovering implicit knowledge linkages in existing scientific literature to provide impetus to innovation and research productivity. Despite significant advancements in LBD research, previous studies contain several open problems and shortcomings that are hindering its progress. The overarching goal of this thesis is to address these issues, not only to enhance the discovery component of LBD, but also to shed light on new directions that can further strengthen the existing understanding of the LBD work ow. In accordance with this goal, the thesis aims to enhance the LBD work ow with a view to ensuring its widespread applicability. The goal of widespread applicability is twofold. Firstly, it relates to the adaptability of the proposed solutions to a diverse range of problem settings. These problem settings are not necessarily application areas that are closely related to the LBD context, but could include a wide range of problems beyond the typical scope of LBD, which has traditionally been applied to scientific literature. Adapting the LBD work ow to problems outside the typical scope of LBD is a worthwhile goal, since the intrinsic objective of LBD research, which is discovering novel linkages in text corpora is valid across a vast range of problem settings. Secondly, the idea of widespread applicability also denotes the capability of the proposed solutions to be executed in new environments. These `new environments' are various academic disciplines (i.e., cross-domain knowledge discovery) and publication languages (i.e., cross-lingual knowledge discovery). The application of LBD models to new environments is timely, since the massive growth of the scientific literature has engendered huge challenges to academics, irrespective of their domain. This thesis is divided into five main research objectives that address the following topics: literature synthesis, the input component, the discovery component, reusability, and portability. The objective of the literature synthesis is to address the gaps in existing LBD reviews by conducting the rst systematic literature review. The input component section aims to provide generalised insights on the suitability of various input types in the LBD work ow, focusing on their role and potential impact on the information retrieval cycle of LBD. The discovery component section aims to intermingle two research directions that have been under-investigated in the LBD literature, `modern word embedding techniques' and `temporal dimension' by proposing diachronic semantic inferences. Their potential positive in uence in knowledge discovery is veri ed through both direct and indirect uses. The reusability section aims to present a new, distinct viewpoint on these LBD models by verifying their reusability in a timely application area using a methodical reuse plan. The last section, portability, proposes an interdisciplinary LBD framework that can be applied to new environments. While highly cost-e cient and easily pluggable, this framework also gives rise to a new perspective on knowledge discovery through its generalisable capabilities. Succinctly, this thesis presents novel and distinct viewpoints to accomplish five main research objectives, enhancing the existing understanding of the LBD work ow. The thesis offers new insights which future LBD research could further explore and expand to create more eficient, widely applicable LBD models to enable broader community benefits.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

強い重力レンズモデルの比較研究

Tohoku University二間瀬敏史課

Cyberinfrastructure for Cosmology and Line-of-Sight Projection in Optical Galaxy Clusters.

Upcoming wide-area sky surveys such as the Dark Energy Survey (DES) offer the power to test the source of cosmic acceleration by placing extremely precise constraints on existing cosmological model parameters. These observational surveys will employ multiple tests based on statistical signatures of galaxies and larger-scale structures such as clusters of galaxies. Simulations of large-scale structure provide the means to maximize the power of sky survey tests by characterizing key sources of systematic uncertainties. This dissertation explores two subjects motivated by these facts. First, it explores how grid-aware cyberinfrastructure needs to be utilized in current and upcoming simulation campaigns that support large-area sky surveys. Second, it shows how line-of-sight projection plays into cosmological analysis based on galaxy cluster counts in the same wide-area sky surveys. In the first part, an Apache Airavata-enabled grid-aware application workflow for managing simulations is described. Results pertaining to efficiency in producing N-body simulations are reported. In the second part, bias in cosmological parameter estimates caused by incorrectly assuming a Gaussian (projection-free) mass--observable relation when the true relation is non-Gaussian due to projection is explored. Projection tends to skew the mass--observable relation of galaxy clusters by creating a small fraction of severely blended systems, those for which the measured observable property of a cluster is strongly boosted relative to the value of its primary host halo. A model motivated by optical cluster-finding applied to the Millennium Simulation is introduced for projection and Fisher information matrix parameter bias forecasts are produced for a DES-like sky survey. The model predicts significant biases in the dark energy density and equation of state parameters. The model additional predicts an increase in uncertainties in dark energy parameters to a factor of about two larger than forecast uncertainties. Additionally, new parameters used to characterize the model degrade uncertainties in the dark energy parameters. Motivated by this result, this dissertation also contains preliminary results for a new projection model meant to reduce bias in cluster analysis based on redMaPPer identified clusters for the DES.PHDPhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99938/1/bmse_1.pd