Bhaskar Mitra Ph D Pdf
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Handbook on the Clinical Treatment of Adopted Adolescents and Young Adults
This collection bridges the voices of international scholars and adopted persons to share knowledge about clinical practice with adopted people in adolescence and early adulthood. Coming at a time when countries are beginning to focus on adoption reform, this handbook is the first to address not only the external, systemic contributions to their developmental complexities but also the underlying, internal meanings of being adopted as children become adolescents and mature into adulthood. It explains how adopted clients differ from those not adopted and emphasizes the need for clinical research on adopted people in this older age group. Exploring how clinicians can understand their client’s clinical needs, it offers specific protocols and frameworks for assessment and necessary modifications in language and treatment. With a foreword by Miriam Steele, chapters examine the legal and sociopolitical cultures, policies, and practices in which adoption is embedded, calling for broad systemic change. Embracing theoretical, conceptual, and global perspectives, this handbook is written for clinicians in all disciplines, at all tiers of practice, administration, and training, identifying the key roles they can potentially play in expanding and better focusing our understanding of the psychology of being adopted.
Simulating Information Retrieval Test Collections
Author: David Hawking
language: en
Publisher: Morgan & Claypool Publishers
Release Date: 2020-09-04
Simulated test collections may find application in situations where real datasets cannot easily be accessed due to confidentiality concerns or practical inconvenience. They can potentially support Information Retrieval (IR) experimentation, tuning, validation, performance prediction, and hardware sizing. Naturally, the accuracy and usefulness of results obtained from a simulation depend upon the fidelity and generality of the models which underpin it. The fidelity of emulation of a real corpus is likely to be limited by the requirement that confidential information in the real corpus should not be able to be extracted from the emulated version. We present a range of methods exploring trade-offs between emulation fidelity and degree of preservation of privacy. We present three different simple types of text generator which work at a micro level: Markov models, neural net models, and substitution ciphers. We also describe macro level methods where we can engineer macro properties of a corpus, giving a range of models for each of the salient properties: document length distribution, word frequency distribution (for independent and non-independent cases), word length and textual representation, and corpus growth. We present results of emulating existing corpora and for scaling up corpora by two orders of magnitude. We show that simulated collections generated with relatively simple methods are suitable for some purposes and can be generated very quickly. Indeed it may sometimes be feasible to embed a simple lightweight corpus generator into an indexer for the purpose of efficiency studies. Naturally, a corpus of artificial text cannot support IR experimentation in the absence of a set of compatible queries. We discuss and experiment with published methods for query generation and query log emulation. We present a proof-of-the-pudding study in which we observe the predictive accuracy of efficiency and effectiveness results obtained on emulated versions of TREC corpora. The study includes three open-source retrieval systems and several TREC datasets. There is a trade-off between confidentiality and prediction accuracy and there are interesting interactions between retrieval systems and datasets. Our tentative conclusion is that there are emulation methods which achieve useful prediction accuracy while providing a level of confidentiality adequate for many applications.
The Content Machine
This ground-breaking study, the first of its kind, outlines a theory of publishing that allows publishing houses to focus on their core competencies in times of crisis. Tracing the history of publishing from the press works of fifteenth-century Germany to twenty-first-century Silicon Valley, via Venice, Beijing, Paris and London, and fusing media theory and business experience, ‘The Content Machine’ offers a new understanding of content, publishing and technology, and defiantly answers those who contend that publishing has no future in a digital age.