46 research outputs found
Interpreting RCT, process evaluation and case study evidence in evaluating the Integrated Group Reading (IGR) programme: a teacher-led, classroom-based intervention for Year 2 and 3 pupils struggling to read
This is the author accepted manuscript. The final version is available from Taylor & Francis (Routledge) via the DOI in this record.Almost 20% of English pupils still experience difficulties in reading despite a predominantly phonics approach that works well for most children, but not for all; so other approaches need to be explored. The IGR programme involves an inclusive approach to targeted teaching led by class teachers using a group-based class organisation and the integration of diverse research-based approaches (language and phonics-based). IGR has been evaluated in thirty-four English schools in five varied local authority areas using a cluster randomised design and a process evaluation. IGR was found to support enjoyment of reading with as much reading gains as the more phonics-oriented programmes used in control classes. Following its use, there were gains in teachers’ self-efficacy in teaching reading, and no negative effects on the class pupils’ reading. This study shows what a more inclusive approach to targeted reading intervention can achieve with a well-resourced programme. Questions can be about the interpretation of RCT findings when it comes to classroom-based educational interventions, and about teacher choice in opting for alternate teaching approaches.Nuffield Foundatio
An innovative classroom reading intervention for Year 2 and 3 pupils who are struggling to learn to read: Evaluating the Integrated Group Reading Programme
Executive summary and project report - May 2018Nuffield Foundatio
Ethnic and regional inequalities in Russian military fatalities in Ukraine: Preliminary findings from crowdsourced data
This is the final version. Available on open access from the Max Planck Institute for Demographic Research via the DOI in this recordData availability: The data and the R code for replication analysis can be found at the Github repository: https://github.com/abessudnov/ruCasualtiesPublic. The names of the deceased servicemen and their implied ethnicity have been removed from the dataset. However, 93% of the records contain links to archived original social media posts and other reports, allowing the information to be verified.Objective: This paper investigates ethnic and regional disparities in fatality rates in the Russian military in 2022‒2023 during the war in Ukraine.
Methods: The analysis uses a new crowdsourced dataset comprising the names of over 20,000 Russian soldiers killed in Ukraine between February 2022 and April 2023. This dataset was compiled by a team of volunteers who gathered information from social media and other accessible sources. The dataset is incomplete and therefore the findings reported in this paper are tentative. Mortality rates and relative risks are estimated by ethnic group and region, and a linear model is fitted to assess the correlation between the ethnic composition of the population, socioeconomic factors, and regional fatality rates.
Results: The study reveals significant disparities in military fatality rates across Russian regions, with the highest mortality observed among soldiers originating from economically disadvantaged areas in Siberia and the Russian Far East and the lowest among soldiers from Moscow and St. Petersburg. Buryats and Tuvans are overrepresented among the fatalities relative to their population share. However, when regional socioeconomic disparities are accounted for, ethnic differences in mortality rates are considerably reduced.
Conclusions: The observed regional and ethnic fatality disparities appear to be driven by socioeconomic inequalities between regions.
Contribution: This paper evaluates social inequalities in fatalities in the Russian military in Ukraine and compares these findings with research on US military casualties
Academisation of Schools in England and Placements of Pupils With Special Educational Needs: An Analysis of Trends, 2011–2017
This is the author accepted manuscript. The final version is available via the DOI in this recordThis paper aims to examine the changes in school composition in England from 2011
to 2017 by school type and school phase; the speed of academisation by region; and
the changes in the proportions of pupils with special educational needs (SEN) at SEN
Support and EHC Plan levels overall. We analyse publicly available school level data
from the National Pupil Database (NPD) to document two simultaneous trends in English
education between 2011 and 2017. First, we observe an increasing percentage of the
schools that have become Academies, especially in the secondary mainstream sector,
but also among primary schools, special schools and pupil referral units. Second, we
document a decreasing percentage of pupils who were classified as having SEN. While
the decrease happened across all types of schools, it was particularly steep in Sponsored
Academies. This evidence does not necessarily imply that the academisation of English
schools has had a negative effect on the inclusion of pupils with SEN. However, the
findings have significance to provide the basis for a more in-depth analysis of these trends
and the causal effects of academisation involving individual and school level analyses.
They can also inform national and local policy review of how pupils are identified as having
SEN and in the context of international moves toward greater inclusive education.Economic and Social Research Council (ESRC
A statistical evaluation of the effects of a structured postdoctoral programme
Published© 2014 Society for Research into Higher Education. Postdoctoral programmes have recently become an important step leading from doctoral education to permanent academic careers in the social sciences. This paper investigates the effects of a large and structured postdoctoral programme in the social sciences on a number of academic and non-academic outcomes of fellows. Propensity score matching is employed to match fellows with applicants with similar characteristics who did not receive the fellowship; then the outcomes in the treatment and control groups are compared. The programme has a statistically significant positive effect on the general life satisfaction of former fellows and their publication activity. It is argued that an active and collegial research environment, with training in academic skills during postdoctoral employment, may improve the academic outcomes of postdoctoral fellows
The tempo of cultural change in the Kostenki Upper Paleolithic : further insights
open access via Cambridge University Press agreement This work was funded by the Leverhulme Trust (AHOB3 and RPG-2012-800). We thank the staff of the ORAU past and present for their careful laboratory work. We also thank the reviewers and Editor-in-Chief for their comments. AB and AS acknowledge Russian Science Foundation grant numbers 20-78-10151 and 18-78-00136, and Russian Foundation of Basic Research grant numbers 18-39-20009, 18-00-00837 and 20-09-00233. We also acknowledge the participation of IHMC RAS (state assignment 0184-2019-0001) and ZIN RAS (state assignment АААА-А19-119032590102-7). We thank the UK Natural Environment Research Council (NERC) for supporting the Oxford node of the National Environmental Isotope Facility (NEIF).Peer reviewedPublisher PD
Predicting perceived ethnicity with data on personal names in Russia
This is the final version. Available on open access from Springer via the DOI in this recordData availability statement:
The research data supporting this publication and the Python code are openly available from Github at: https://github.com/abessudnov/ruEthnicNamesPublicIn this paper, we develop a machine learning classifier that predicts perceived ethnicity from data on personal names for major ethnic groups populating Russia. We collect data from VK, the largest Russian social media website. Ethnicity was coded from languages spoken by users and their geographical location, with the data manually cleaned by crowd workers. The classifier shows the accuracy of 0.82 for a scheme with 24 ethnic groups and 0.92 for 15 aggregated ethnic groups. It can be used for research on ethnicity and ethnic relations in Russia, with the data sets that have personal names but not ethnicity