Teacher Candidates as Writers: What is the Relationship Between Writing Experiences and Pedagogical Practice

Both teacher candidates (TCs) and practicing teachers are asked to engage in personal writing experiences as means of learning about writing instruction. Yet, research on the relationship between writing and teaching writing provides variable, sometimes contradictory, results. This study investigated the relationship between TCs’ experiences writing a personal narrative in an undergraduate teacher education course and how they read and respond to a second grader’s personal narrative. Results indicate that, initially, many TCs did not draw on their writing experiences to inform how they analyzed, interpreted, and responded to the student’s composition. However, when specifically prompted to think about their writing experiences in the course, 89% were able to notice features in the child’s writing that they had learned to include in their own writing. The authors offer a theoretical framework to explain the results and argue that the framework could be used to guide writing teacher educators as they design writing experiences for teacher candidates. This study provides insights into teachers as writers and how writing experiences impact teachers candidates’ writing pedagogy

The Evolution of Concentrated Ownership in India Broad patterns and a History of the Indian Software Industry

As in many countries (Canada, France, Germany, Japan, Italy, Sweden), concentrated ownership is a ubiquitous feature of the Indian private sector over the past seven decades. Yet, unlike in most countries, the identity of the primary families responsible for the concentrated ownership changes dramatically over time, perhaps even more than it does in the U.S. during the same time period. It does not appear that concentrated ownership in India is entirely associated with the ills that the literature has recently ascribed to concentrated ownership in emerging markets. If the concentrated owners are not exclusively, or even primarily, engaged in rent-seeking and entry-deterring behavior, concentrated ownership may not be inimical to competition. Indeed, as a response to competition, we argue that at least some Indian families the concentrated owners in question have consistently tried to use their business group structures to launch new ventures. In the process they have either failed hence the turnover in identity or reinvented themselves. Thus concentrated ownership is a result, rather than a cause, of inefficiencies in capital markets. Even in the low capital-intensity, relatively unregulated setting of the Indian software industry, we find that concentrated ownership persists in a privately successful and socially useful way. Since this setting is the least hospitable to the existence of concentrated ownership, we interpret our findings as a lower bound on the persistence of concentrated ownership in the economy at large.

The Iliad’s big swoon: a case of innovation within the epic tradition

In book 5 of the Iliad Sarpedon suffers so greatly from a wound that his ‘‘ψυχή leaves him’. Rather than dying, however, Sarpedon lives to fight another day. This paper investigates the phrase τὸν δὲ λίπε ψυχή in extant archaic Greek poetry to gain a sense of its traditional referentiality and better assess the meaning of Sarpedon’s swoon. Finding that all other instances of the ψυχή leaving the body signify death, it suggests that the Iliad exploits a traditional unit of utterance to flag up the importance of Sarpedon to this version of the Troy story

Scalable Tensor Factorizations for Incomplete Data

The problem of incomplete data - i.e., data with missing or unknown values - in multi-way arrays is ubiquitous in biomedical signal processing, network traffic analysis, bibliometrics, social network analysis, chemometrics, computer vision, communication networks, etc. We consider the problem of how to factorize data sets with missing values with the goal of capturing the underlying latent structure of the data and possibly reconstructing missing values (i.e., tensor completion). We focus on one of the most well-known tensor factorizations that captures multi-linear structure, CANDECOMP/PARAFAC (CP). In the presence of missing data, CP can be formulated as a weighted least squares problem that models only the known entries. We develop an algorithm called CP-WOPT (CP Weighted OPTimization) that uses a first-order optimization approach to solve the weighted least squares problem. Based on extensive numerical experiments, our algorithm is shown to successfully factorize tensors with noise and up to 99% missing data. A unique aspect of our approach is that it scales to sparse large-scale data, e.g., 1000 x 1000 x 1000 with five million known entries (0.5% dense). We further demonstrate the usefulness of CP-WOPT on two real-world applications: a novel EEG (electroencephalogram) application where missing data is frequently encountered due to disconnections of electrodes and the problem of modeling computer network traffic where data may be absent due to the expense of the data collection process

How binding are legal limits? Transitions from temporary to permanent work in Spain

This paper studies the duration pattern of …xed-term contracts and the determinants of their conversion into permanent ones in Spain, where the share of …xed-term employment is the highest in Europe. We estimate a duration model for temporary employment, with competing risks of terminating into permanent employment versus alternative states, and ‡exible duration dependence. We …nd that conversion rates are generally below 10%. Our estimated conversion rates roughly increase with tenure, with a pronounced spike at the legal limit, when there is no legal way to retain the worker on a temporary contract. We argue that estimated di¤erences in conversion rates across categories of workers can stem from di¤erences in worker outside options and thus the power to credibly threat to quit temporary jobs.Fixed-term contracts, duration models

Using Machine Learning to Predict the Evolution of Physics Research

The advancement of science as outlined by Popper and Kuhn is largely qualitative, but with bibliometric data it is possible and desirable to develop a quantitative picture of scientific progress. Furthermore it is also important to allocate finite resources to research topics that have growth potential, to accelerate the process from scientific breakthroughs to technological innovations. In this paper, we address this problem of quantitative knowledge evolution by analysing the APS publication data set from 1981 to 2010. We build the bibliographic coupling and co-citation networks, use the Louvain method to detect topical clusters (TCs) in each year, measure the similarity of TCs in consecutive years, and visualize the results as alluvial diagrams. Having the predictive features describing a given TC and its known evolution in the next year, we can train a machine learning model to predict future changes of TCs, i.e., their continuing, dissolving, merging and splitting. We found the number of papers from certain journals, the degree, closeness, and betweenness to be the most predictive features. Additionally, betweenness increases significantly for merging events, and decreases significantly for splitting events. Our results represent a first step from a descriptive understanding of the Science of Science (SciSci), towards one that is ultimately prescriptive.Comment: 24 pages, 10 figures, 4 tables, supplementary information is include