The idea here is that cabinet appointments are determined by the need to reflect the various competing interests of parliament in the cabinet. The Australian Federal Parliament consists of the House of Representatives (Lower House) and the Senate (Upper House). The importance of representative factors is that the literature on the appointment of the minister emphasizes them as important factors in determining the appointment to the ministry.
Abstractness/concreteness is measured by scoring parliamentarians' speech samples based on the average concreteness of the nouns in the samples. With these representational and personal variables, I have captured a considerable number of the types of factors likely to be associated with a cabinet appointment. For elections held in the middle of the year, changes in the composition of the parliament are not reflected in the data until 1
In the case of the personal variables, Parlage is calculated by subtracting Experience from Age, so the inclusion of all three variables is unlikely for more information. It is clear that this model is not efficient because the ability of the model to detect CMs is not clear. The apparent overall model accuracy of 75% - 70% is due to the high default specificity.
Although the classification accuracy of the model is not efficient, the analysis of the significance of the variables in the model gives us another insight into the causal effects of the independent variables. What this suggests is that a model consisting of the personal variables and the main geographic variables should provide a good fit. Ninth is the last personal variable, Law, also accounting for 40% of relative importance.
The reason for this is that studies such as Kam et al (2010) show that there is a non-linear relationship between. Despite this finding, it is worth noting that in the case of age, it has been found that when age is modeled using a linear equation, there is a negative relationship between age and the probability of cabinet appointment; where Age is modeled as a quadratic equation, there is a negative quadratic relationship between age and cabinet appointment with an inflection point sometime in the 40s. In the context of the current study, suffice it to say that the patterns discovered in other jurisdictions and domains appear to be present in the Australian federal arena.
O'Malley (2006) found that the number of years since first election (experience) is positively associated with ministerial appointment in the Irish Parliament. In November 2001, a reshuffle was described in The Canberra Times in the following terms: "Howard's desire to push his own type of coalition politician, the need for new talent and the need to retain experience at the highest level were the forces behind his reshuffle" (Canberra Times 25. November 2001, p. 20) There is a negative influence of the Parlage variables (Parlage and Parlage Squared) on the dependent variable.
Buck (1963) pointed out that in the House of Commons of the British Parliament, “[m]eministerial candidates take advantage of an early opportunity to begin to advance up the official hierarchy and into . In the US context, legally trained individuals have been found to be more likely to rise to senior positions on the most prestigious boards than non-legally trained individuals (Miller 1995). The main benefit of this is that hypothetical thinking prepares individuals to consider alternatives (Galinsky and Moskowitz 2000: 385).
This suggests that CMs are likely to have a relatively high level of psychological construction (Trope and Lieberman 2010). Psychological level of construal (PLC) is the extent to which an individual conceptualizes their experience from a global (abstract) as opposed to a local (concrete) perspective. The link between PLC and language use is that those with a higher level of PLC (that is, a more global outlook) tend to use more abstract language than those with a lower level of PLC.
The important point is that concreteness as measured in linguistic samples can be used as a proxy for the level of construction. Human history is associated with the expansion of horizons - the traversing of greater spatial distances (eg the discovery of new continents, space travel); the formation of larger social groups (families versus cities versus states versus global institutions); planning and investing in the more distant future; and reaches further back into the past. Human development in the first years of life involves acquiring the ability to plan for the more distant future, to consider possibilities that are not present, and to consider the perspective of more distant people…”.
The implication of this is that CMs, who display lower levels of concreteness in their speech examples, are likely to have higher levels of PLC and are therefore more likely to exhibit the characteristics associated with "expanding horizons". The implication of this for modeling in general is that key geographic regions should be included in the models, because if there is any bias in cabinet appointments, including key geographic regions in the model will increase classification accuracy.
Summary and Conclusion
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