Early intervention and identification: finding the family

Following the growth of early intervention programmes for very young children with special educational needs and their families in the 1960s and 1970s, there came a reflective period during which the efficacy of early intervention was questioned (Marfo and Kysela, 1985). This was followed in the 1990s by a challenge to narrow interpretations of effectiveness in favour of a more holistic analysis of the benefits of intervention programmes for the whole family (Buckley, 1994). It was suggested that more attention should be given to the quality of relationships being established between the child and family members and the context in which early intervention programmes are delivered. Families themselves have begun to assert this (Fitton, 1994; Hebden, 1985) and recent legislation, both in the UK and abroad, has underlined the need for strong interdisciplinary approaches to meet the diversity of special educational needs that exist in our child population. This multidisciplinary approach necessitates a change of focus. It recognises the parent, with their child, as central, implicit and fundamental to the early intervention team, and endorses their right to request appropriate access to services. The model also gives parents the freedom to expand the concept of the family to include extended family and friends (for example, Ballard, 1994). Other parent-empowering initiatives include parent-to-parent schemes (Hornby, 1989 and 1995) which increase the capacity of families to provide resources to other families. The days of professionals as experts have passed. This change in approach to families of children with special educational needs will challenge some professionals and demand a radical reappraisal of the structure of their role and their style of delivery of early intervention, but, if we want quality early intervention, such a review is fundamental to re-establishing its place as a valid and valuable resource to families.     

Domain specificity of fairness judgments in economic transactions

Two studies eliciting fairness judgments about hypothetical economic transactions examined whether fairness judgments were influenced by who was being judged. The first study replicated the results of Kahneman et al. (Am. Econom. Rev. 76 (1986a) 728–741; J. Business 59 (1986b) S285–S300) that people judge certain actions by firms as unfair, but it also demonstrated that people judge parallel actions by individuals as fair. The second study suggested that people apply different standards to individuals and firms because of presumed differences between them in wealth, power, and size. When firms were portrayed as no more powerful or wealthy than individuals, differences in fairness judgments were eliminated. Further, respondents were less inclined to judge the behavior of a firm harshly as perpetrator of an unfair act when the firm was identified with an individual than when it was large and anonymous, and they were more inclined to judge the behavior of an individual harshly as perpetrator of an unfair act when the action injured a firm with a clearly identified individual than when the firm was large and anonymous.     

Social Movement,Action and Change: The Influence of Women's Movements on City Government in Mumbai and London

This article reports on a research investigation into gender and local government in Mumbai in India and London in England. In both these cities female representation at the political level stands at around one third, achieved in London slowly in recent years and in Mumbai more rapidly through the adoption of a quota, or seat reservation system, implemented in 1992. In considering the experience of the women concerned it is argued that their presence and aspirations have been influenced through the networks of their respective women's movements, operating through civil society and the local state. In considering the ways in which they organize and manage the duties of office and their gendered identities, as well as in their focus on the most disadvantaged in their communities and in their dealings with others, the part played by social movements in influencing change is examined.     

Reconsidering union wage effects: Surveying new evidence on an old topic

I examine evidence on private sector union wage gaps in the United States. The consensus opinion among labor economists of an average union premium of roughly 15 percent is called into question. Two forms of measurement error bias downward standard wage gap estimates. Match bias results from Census earnings imputation procedures that do not include union status as a match criterion. Downward bias is roughly equal to the proportion of workers with imputed earnings, currently about 30 percent. Misclassification of union status causes additional attenuation in union gap measures. This bias has worsened as private sector density has declined, since an increasing proportion of workers designated as union are instead nonunion workers. Corrections for misclassification and match bias lead to estimated union gaps substantially higher than standard estimates, but with less of a downward trend since the mid 1980s. Private sector union gaps corrected for these biases are estimated from the CPS for 1973–2001. The uncorrected estimate for 2001 is .13 log points. Correction for match bias increases the gap to .18 log points; further correction for misclassification bias, based on an assumed 2 percent error rate, increases the gap to .24. Reexamination of the skill-upgrading hypothesis leads to the conclusion that higher union gap estimates are plausible. The conventional wisdom of a 15 percent union wage premium warrants reexamination.     

Health Policy and the Politics of Evidence

National decisions on the drugs, treatments and medical devices that should be funded through public expenditure are a fundamental element of health policy. But despite a political emphasis upon evidence‐based policy, the results of rigorous clinical trials and statistical modelling techniques rarely speak for themselves. So, does the pre‐eminence traditionally accorded to quantitative data in the medical field underpin policy decisions on a consistent basis? Or are more subtle, less transparent characteristics of context and interaction evident in the shaping of attendant decisions? This article considers these questions by drawing on a study of decision‐making in the National Institute for Health and Clinical Excellence (NICE)—an organization established by the British government in 1999 to decide whether selected health technologies should be made available throughout the National Health Service in England and Wales. In broad terms, the findings point to the primacy of arguments based on quantitatively oriented, experimentally derived data but also to a discursive hegemony of clinicians and health economists in mediating, including or debarring more qualitative, experientially based evidence. A more complex, dynamic understanding of policy governance in the field of health technology appraisal—founded on a discursive appropriation of the idea of the “common good”—goes some way to explaining the persistence of this hegemony despite an avowedly inclusive, plural approach to decision‐making.     

Distance sampling methodology

We consider the method of distance sampling described by Buckland, Anderson, Burnham and Laake in 1993. We explore the properties of the methodology in simple cases chosen to allow direct and accessible comparisons of distance sampling in the design- and model-based frameworks. In particular, we obtain expressions for the bias and variance of the distance sampling estimator of object density and for the expected value of the recommended analytic variance estimator within each framework. These results enable us to clarify aspects of the performance of the methodology which may be of interest to users and potential users of distance sampling.     

The multivariate alpha-power model

The main object of this paper is to propose a multivariate extension to the alpha-power model which is an alternative to the multivariate skew-normal model (Arellano-Valle and Azzalini, 2008). It also extends the power-normal model discussed in Gupta and Gupta (2008) by making it more flexible. Inference is dealt with by using the likelihood approach and a pseudo-likelihood approach based on conditional distributions which, although slightly less efficient, is simpler to implement. An application to a real data set is used to demonstrate the usefulness of the extension.