Aboriginal women in education : Honouring our experiences a vision of access to and success within the university

This thesis explores Aboriginal women's access to and success within universities through an examination of Aboriginal women's educational narratives, along with input from key service providers from both the Aboriginal and non-Aboriginal community. Implemented through the Wildfire Research Method, participants engaged in a consensusbased vision of accessible education that honours the spiritual, emotional, intellectual, and physical elements necessary for the success of Aboriginal women in university. This study positions Aboriginal women as agents of social change by allowing them to define their own needs and offer viable solutions to those needs. Further, it connects service providers from the many disconnected sectors that implicate Aboriginal women's education access. The realities of Aboriginal women are contextualized through historical, sociocultural, and political analyses, revealing the need for a decolonizing educational approach. This fosters a shift away from a deficit model toward a cultural and linguistic assets based approach that emphasizes the need for strong cultural identity formation. Participants revealed academic, cultural, and linguistic barriers and offered clear educational specifications for responsive and culturally relevant programming that will assist Aboriginal women in developing and maintaining strong cultural identities. Findings reveal the need for curriculum that focuses on decolonizing and reclaiming Aboriginal women's identities, and program outcomes that encourage balance between two worldviews-traditional and academic-through the application of cultural traditions to modern contexts, along with programming that responds to the immediate needs of Aboriginal women such as childcare, housing, and funding, and provide an opportunity for universities and educators to engage in responsive and culturally grounded educational approaches.

The Reluctance of Cattle to Change a Learned Choice May Confound Preference Tests

The conclusion of the article states "it appears that previously learned choices may affect future choices in Y-mazes for cattle. Another area that needs to be researched is the effects of a mildly aversive treatment versus a severely aversive treatment on the tendency of a bovine to resist changing a learned choice".

The vision of sir Launfal and other poems

UANL

The vision of Sir Launfal, and other poems

UANL

Simulation-Based Finite and Large Sample Tests in Multivariate Regressions.

In the context of multivariate linear regression (MLR) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose a general method for constructing exact tests of possibly nonlinear hypotheses on the coefficients of MLR systems. For the case of uniform linear hypotheses, we present exact distributional invariance results concerning several standard test criteria. These include Wilks' likelihood ratio (LR) criterion as well as trace and maximum root criteria. The normality assumption is not necessary for most of the results to hold. Implications for inference are two-fold. First, invariance to nuisance parameters entails that the technique of Monte Carlo tests can be applied on all these statistics to obtain exact tests of uniform linear hypotheses. Second, the invariance property of the latter statistic is exploited to derive general nuisance-parameter-free bounds on the distribution of the LR statistic for arbitrary hypotheses. Even though it may be difficult to compute these bounds analytically, they can easily be simulated, hence yielding exact bounds Monte Carlo tests. Illustrative simulation experiments show that the bounds are sufficiently tight to provide conclusive results with a high probability. Our findings illustrate the value of the bounds as a tool to be used in conjunction with more traditional simulation-based test methods (e.g., the parametric bootstrap) which may be applied when the bounds are not conclusive.

Exact Tests for Contemporaneous Correlation of Disturbances in Seemingly Unrelated Regressions.

This paper proposes finite-sample procedures for testing the SURE specification in multi-equation regression models, i.e. whether the disturbances in different equations are contemporaneously uncorrelated or not. We apply the technique of Monte Carlo (MC) tests [Dwass (1957), Barnard (1963)] to obtain exact tests based on standard LR and LM zero correlation tests. We also suggest a MC quasi-LR (QLR) test based on feasible generalized least squares (FGLS). We show that the latter statistics are pivotal under the null, which provides the justification for applying MC tests. Furthermore, we extend the exact independence test proposed by Harvey and Phillips (1982) to the multi-equation framework. Specifically, we introduce several induced tests based on a set of simultaneous Harvey/Phillips-type tests and suggest a simulation-based solution to the associated combination problem. The properties of the proposed tests are studied in a Monte Carlo experiment which shows that standard asymptotic tests exhibit important size distortions, while MC tests achieve complete size control and display good power. Moreover, MC-QLR tests performed best in terms of power, a result of interest from the point of view of simulation-based tests. The power of the MC induced tests improves appreciably in comparison to standard Bonferroni tests and, in certain cases, outperforms the likelihood-based MC tests. The tests are applied to data used by Fischer (1993) to analyze the macroeconomic determinants of growth.