It Don’t Mean a Thing... Simultaneous Interpretation Quality and User Satisfaction

The issue of quality has been extensively discussed in Interpreting Studies (IS). Quality is subjective, ineffable and cultural. As the “aspiring-to-science community” (hereafter “ATSC”1) defines “scientific” as empirical, quantifiable and objective2, it is bound to struggle when dealing with such a concept. Yet, precisely because it stipulates that a scientific approach requires a quantifiable dimension, it has to try and define quality in an objective manner. Shackled by its postulates, the ATSC has drawn upon two approaches that have predictably come short. One vainly seeks to define quality and subsequently “objective and quantifiable” criteria to assess it. The other claims to draw on marketing and strives to measure user satisfaction, primarily through questionnaires. The most advanced work in marketing, however, has taken on board the findings of cognitiv

Using Word Embedding to Evaluate the Coherence of Topics from Twitter Data

Scholars often seek to understand topics discussed on Twitter using topic modelling approaches. Several coherence metrics have been proposed for evaluating the coherence of the topics generated by these approaches, including the pre-calculated Pointwise Mutual Information (PMI) of word pairs and the Latent Semantic Analysis (LSA) word representation vectors. As Twitter data contains abbreviations and a number of peculiarities (e.g. hashtags), it can be challenging to train effective PMI data or LSA word representation. Recently, Word Embedding (WE) has emerged as a particularly effective approach for capturing the similarity among words. Hence, in this paper, we propose new Word Embedding-based topic coherence metrics. To determine the usefulness of these new metrics, we compare them with the previous PMI/LSA-based metrics. We also conduct a large-scale crowdsourced user study to determine whether the new Word Embedding-based metrics better align with human preferences. Using two Twitter datasets, our results show that the WE-based metrics can capture the coherence of topics in tweets more robustly and efficiently than the PMI/LSA-based ones

Topic-centric Classification of Twitter User's Political Orientation

In the recent Scottish Independence Referendum (hereafter, IndyRef), Twitter offered a broad platform for people to express their opinions, with millions of IndyRef tweets posted over the campaign period. In this paper, we aim to classify people's voting intentions by the content of their tweets---their short messages communicated on Twitter. By observing tweets related to the IndyRef, we find that people not only discussed the vote, but raised topics related to an independent Scotland including oil reserves, currency, nuclear weapons, and national debt. We show that the views communicated on these topics can inform us of the individuals' voting intentions ("Yes"--in favour of Independence vs. "No"--Opposed). In particular, we argue that an accurate classifier can be designed by leveraging the differences in the features' usage across different topics related to voting intentions. We demonstrate improvements upon a Naive Bayesian classifier using the topics enrichment method. Our new classifier identifies the closest topic for each unseen tweet, based on those topics identified in the training data. Our experiments show that our Topics-Based Naive Bayesian classifier improves accuracy by 7.8% over the classical Naive Bayesian baseline

Thermoelectric and Magnetothermoelectric Transport Measurements of Graphene

The conductance and thermoelectric power (TEP) of graphene is simultaneously measured using microfabricated heater and thermometer electrodes. The sign of the TEP changes across the charge neutrality point as the majority carrier density switches from electron to hole. The gate dependent conductance and TEP exhibit a quantitative agreement with the semiclassical Mott relation. In the quantum Hall regime at high magnetic field, quantized thermopower and Nernst signals are observed and are also in agreement with the generalized Mott relation, except for strong deviations near the charge neutrality point

Patient and practice characteristics predicting attendance and completion at a specialist weight management service in the UK: a cross-sectional study

Objective: To determine the association between patient and referring practice characteristics and attendance and completion at a specialist health service weight management service (WMS). Design: Cross-sectional study. Setting: Regional specialist WMS located in the West of Scotland. Participants: 9677 adults with obesity referred between 2012 and 2014; 3250 attending service and 2252 completing. Primary and secondary outcome measures: Primary outcome measure was attendance at the WMS; secondary outcome was completion, defined as attending four or more sessions. Analysis: Multilevel binary logistic regression models constructed to determine the association between patient and practice characteristics and attendance and completion. Results: Approximately one-third of the 9677 obese adults referred attended at least one session (n=3250, 33.6%); only 2252 (23%) completed by attending four or more sessions. Practice referrals ranged from 1 to 257. Patient-level characteristics were strongest predictors of attendance; odds of attendance increased with age (OR 4.14, 95% CI 3.27 to 5.26 for adults aged 65+ compared with those aged 18–24), body mass index (BMI) category (OR 1.83, 95% CI 1.56 to 2.15 for BMI 45+ compared with BMI 30–35) and increasing affluence (OR 1.96, 95% CI 1.17 to 3.28). Practice-level characteristics most strongly associated with attendance were being a non-training practice, having a larger list size and not being located in the most deprived areas. Conclusions: There was wide variation in referral rates across general practice, suggesting that there is still much to do to improve engagement with weight management by primary care practitioners. The high attrition rate from referral to attendance and from attendance to completion suggests ongoing barriers for patients, particularly those from the most socioeconomically deprived areas. Patient and practice-level characteristics can help us understand the observed variation in attendance at specialist WMS following general practitioner (GP) referral and the underlying explanations for these differences merit further investigation

Public Safety through Private Action: An economic assessment of BIDs, locks, and citizen cooperation

Given the central role of private individuals and firms in determining the effectiveness of the criminal justice system, and the quality and availability of criminal opportunities, private actions arguably deserve a central role in the analysis of crime and crime prevention policy. But the leading scholarly commentaries on the crime drop during the 1990s have largely ignored the role of the private sector, as have policymakers. Among the potentially relevant trends: growing reporting rates (documented in this paper); the growing sophistication and use of alarms, monitoring equipment and locks; the considerable increase in the employment of private security guards; and the decline in the use of cash. Private actions of this sort have the potential to both reduce crime rates and reduce arrests and imprisonment. Well-designed regulations and programs can encourage effective private action. One creative method to harness private action to cost-effective crime control is the creation of business improvement districts (BIDs). Our quasi-experimental analysis of Los Angeles BIDs demonstrates that the social benefits of BID expenditures on security are a large multiple (about 20) of the private expenditures. Creation and operation of effective BIDs requires a legal infrastructure that helps neighborhoods solve the collective action problem.

Votes on Twitter: assessing candidate preferences and topics of discussion during the 2016 U.S. presidential election

Social media offers scholars new and innovative ways of understanding public opinion, including citizens' prospective votes in elections and referenda. We classify social media users' preferences over the two U.S. presidential candidates in the 2016 election using Twitter data and explore the topics of conversation among proClinton and proTrump supporters. We take advantage of hashtags that signaled users' vote preferences to train our machine learning model which employs a novel classifier-a Topic Based Naive Bayes model-that we demonstrate improves on existing classifiers. Our findings demonstrate that we are able to classify users with a high degree of accuracy and precision. We further explore the similarities and divergences among what proClinton and proTrump users discussed on Twitter

Employer and employment agency attitudes towards employing individuals with mental health needs

Background: The positive benefits of paid employment for individuals with mental health needs are well known yet many still remain unemployed (Perkins & Rinaldi, (2002). Unemployment rates among patients with long-term mental health problems: A decade of rising unemployment. Psychiatric Bulletin, 26(8), 295–298.).\ud \ud Aims: Attitudes of employers and employment agencies that may provide short-term contracts to individuals with mental health needs are important to understand if these individuals are to be given access to paid employment.\ud \ud Methods: A mixed methods approach was used to investigate this phenomenon comprising of interviews and a follow-up survey. Interviews were conducted with 10 employment agencies and 10 employers. The results of these interviews then informed a follow-up survey of 200 businesses in Gloucestershire.\ud \ud Results: The findings demonstrated that employment agencies would consider putting forward individuals with previous mental health needs to employers. However, employers had a high level of concern around employing these individuals. Employers reported issues of trust, needing supervision, inability to use initiative and inability to deal with the public for individuals with either existing or previous mental health needs.\ud \ud Conclusions: The findings of this research suggest a need for employers to have more accurate information regarding hiring individuals with mental health needs