Relevant bibliographies by topics / Unbiased Learning

Academic literature on the topic 'Unbiased Learning'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Unbiased Learning.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Unbiased Learning"

1

Ai, Qingyao, Tao Yang, Huazheng Wang, and Jiaxin Mao. "Unbiased Learning to Rank." ACM Transactions on Information Systems 39, no. 2 (March 2021): 1–29. http://dx.doi.org/10.1145/3439861.

Full text
Abstract:
How to obtain an unbiased ranking model by learning to rank with biased user feedback is an important research question for IR. Existing work on unbiased learning to rank (ULTR) can be broadly categorized into two groups—the studies on unbiased learning algorithms with logged data, namely, the offline unbiased learning, and the studies on unbiased parameters estimation with real-time user interactions, namely, the online learning to rank. While their definitions of unbiasness are different, these two types of ULTR algorithms share the same goal—to find the best models that rank documents based on their intrinsic relevance or utility. However, most studies on offline and online unbiased learning to rank are carried in parallel without detailed comparisons on their background theories and empirical performance. In this article, we formalize the task of unbiased learning to rank and show that existing algorithms for offline unbiased learning and online learning to rank are just the two sides of the same coin. We evaluate eight state-of-the-art ULTR algorithms and find that many of them can be used in both offline settings and online environments with or without minor modifications. Further, we analyze how different offline and online learning paradigms would affect the theoretical foundation and empirical effectiveness of each algorithm on both synthetic and real search data. Our findings provide important insights and guidelines for choosing and deploying ULTR algorithms in practice.
APA, Harvard, Vancouver, ISO, and other styles
2

Vydiswaran, V. G. Vinod, ChengXiang Zhai, Dan Roth, and Peter Pirolli. "Unbiased learning of controversial topics." Proceedings of the American Society for Information Science and Technology 49, no. 1 (2012): 1–4. http://dx.doi.org/10.1002/meet.14504901291.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Backus, B. T. "Optimal learning rates for unbiased perception." Journal of Vision 3, no. 9 (March 16, 2010): 175. http://dx.doi.org/10.1167/3.9.175.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Breeden, Joseph L., and Eugenia Leonova. "Creating Unbiased Machine Learning Models by Design." Journal of Risk and Financial Management 14, no. 11 (November 22, 2021): 565. http://dx.doi.org/10.3390/jrfm14110565.

Full text
Abstract:
Unintended bias against protected groups has become a key obstacle to the widespread adoption of machine learning methods. This work presents a modeling procedure that carefully builds models around protected class information in order to make sure that the final machine learning model is independent of protected class status, even in a nonlinear sense. This procedure works for any machine learning method. The procedure was tested on subprime credit card data combined with demographic data by zip code from the US Census. The census data serves as an imperfect proxy for borrower demographics but serves to illustrate the procedure.
APA, Harvard, Vancouver, ISO, and other styles
5

Li, Ming, Shuo Zhu, Chunxu Li, and Wencang Zhao. "Target unbiased meta-learning for graph classification." Journal of Computational Design and Engineering 8, no. 5 (September 15, 2021): 1355–66. http://dx.doi.org/10.1093/jcde/qwab050.

Full text
Abstract:
Abstract Even though numerous works focus on the few-shot learning issue by combining meta-learning, there are still limits to traditional graph classification problems. The antecedent algorithms directly extract features from the samples, and do not take into account the preference of the trained model to the previously “seen” targets. In order to overcome the aforementioned issues, an effective strategy with training an unbiased meta-learning algorithm was developed in this paper, which sorted out problems of target preference and few-shot under the meta-learning paradigm. First, the interactive attention extraction module as a supplement to feature extraction was employed, which improved the separability of feature vectors, reduced the preference of the model for a certain target, and remarkably improved the generalization ability of the model on the new task. Second, the graph neural network was used to fully mine the relationship between samples to constitute graph structures and complete image classification tasks at a node level, which greatly enhanced the accuracy of classification. A series of experimental studies were conducted to validate the proposed methodology, where the few-shot and semisupervised learning problem has been effectively solved. It also proved that our model has better accuracy than traditional classification methods on real-world datasets.
APA, Harvard, Vancouver, ISO, and other styles
6

Jia, Zhen, Zhang Zhang, Liang Wang, Caifeng Shan, and Tieniu Tan. "Deep Unbiased Embedding Transfer for Zero-Shot Learning." IEEE Transactions on Image Processing 29 (2020): 1958–71. http://dx.doi.org/10.1109/tip.2019.2947780.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Wang, Zong-Hui, Zi-Qian Lu, and Zhe-Ming Lu. "Unbiased hybrid generation network for zero-shot learning." Electronics Letters 56, no. 18 (September 3, 2020): 929–31. http://dx.doi.org/10.1049/el.2020.1594.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Shamsi, Zahra, and Diwakar Shukla. "Efficient Unbiased Sampling of Protein Dynamics using Reinforcement Learning." Biophysical Journal 114, no. 3 (February 2018): 673a. http://dx.doi.org/10.1016/j.bpj.2017.11.3630.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Premo, L. S., and Jonathan B. Scholnick. "The Spatial Scale of Social Learning Affects Cultural Diversity." American Antiquity 76, no. 1 (January 2011): 163–76. http://dx.doi.org/10.7183/0002-7316.76.1.163.

Full text
Abstract:
Sewall Wright's (1943) concept of isolation by distance is as germane to cultural transmission as genetic transmission. Yet there has been little research on how the spatial scale of social learning—the geographic extent of cultural transmission—affects cultural diversity. Here, we employ agent-based simulation to study how the spatial scale of unbiased social learning affects selectively neutral cultural diversity over a range of population sizes and densities. We show that highly localized unbiased cultural transmission may be easily confused with a form of biased cultural transmission, especially in low-density populations. Our results have important implications for how archaeologists infer mechanisms of cultural transmission from diversity estimates that depart from the expectations of neutral theory.
APA, Harvard, Vancouver, ISO, and other styles
10

Guo, Fan, Weiqing Li, Ziqi Shen, and Xiangyu Shi. "MTCLF: A multitask curriculum learning framework for unbiased glaucoma screenings." Computer Methods and Programs in Biomedicine 221 (June 2022): 106910. http://dx.doi.org/10.1016/j.cmpb.2022.106910.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Unbiased Learning"

1

Ribeiro, de Mello Carlos Eduardo. "Active Learning : an unbiased approach." Phd thesis, Châtenay-Malabry, Ecole centrale de Paris, 2013. http://tel.archives-ouvertes.fr/tel-01000266.

Full text
Abstract:
Active Learning arises as an important issue in several supervised learning scenarios where obtaining data is cheap, but labeling is costly. In general, this consists in a query strategy, a greedy heuristic based on some selection criterion, which searches for the potentially most informative observations to be labeled in order to form a training set. A query strategy is therefore a biased sampling procedure since it systematically favors some observations by generating biased training sets, instead of making independent and identically distributed draws. The main hypothesis of this thesis lies in the reduction of the bias inherited from the selection criterion. The general proposal consists in reducing the bias by selecting the minimal training set from which the estimated probability distribution is as close as possible to the underlying distribution of overall observations. For that, a novel general active learning query strategy has been developed using an Information-Theoretic framework. Several experiments have been performed in order to evaluate the performance of the proposed strategy. The obtained results confirm the hypothesis about the bias, showing that the proposal outperforms the baselines in different datasets.
APA, Harvard, Vancouver, ISO, and other styles
2

Chandrasekaran, Santosh. "Unbiased, High-Throughput Electron Microscopy Analysis of Experience-Dependent Synaptic Changes." Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/462.

Full text
Abstract:
Neocortical circuits can adapt to changes in sensory input by modifying the strength or number of synapses. These changes have been well-characterized electrophysiologically in primary somatosensory (barrel) cortex of rodents across different ages and with different patterns of whisker stimulation. Previous work from our lab has identified layer-specific critical periods for synaptic potentiation after selective whisker experience (SWE), where all but one row of facial whiskers has been removed. Although whole-cell patch-clamp recording methods enable a mechanistic understanding of how synaptic plasticity can occur in vivo, they are painstakingly slow, typically focus on a small number of observed events, and are focused on a single pathway or restricted anatomical area. For example, most studies of plasticity in barrel cortex have focused on analyses of experience-dependent synaptic changes in layer 4 and layer 2/3, at a single time point, but it is unclear whether such changes are limited to these layers, or whether they persist over long time periods. Here we employ an established electron-microscopic technique that selectively intensifies synaptic contacts, in combination with unbiased, automated synapse detection, to broadly explore experience-dependent changes in synaptic size and density across many neocortical layers, regions, and time periods in a high-throughput fashion. To validate the method, we focused on imaging synaptic contacts at time points surrounding the critical period for strengthening of excitatory synapses in mouse barrel cortex, and compared these to electrophysiological analyses that show a doubling of synaptic events targeting layer 2/3 pyramidal neurons following SWE. We found that the pattern of occurrence of synapses across the cortical layers is significantly different following SWE. Also, an increase in length was observed specifically in layer 3 synapses. Furthermore, we uncovered potential bidirectional plasticity in L6 synapses depending on the developmental state of circuit and a potential critical period onset for L5A synapse at PND 18. The high resolution imaging and unbiased synapse detection has enabled us to potentially tease apart synaptic changes that occur in a laminar specific fashion. This high-throughput method will facilitate analysis of experience-dependent changes in synaptic density by age, sensory experience, genotype, pharmacological treatments or behavioral training, and will enable classification of synaptic structure to identify key parameters that can be changed by these variables.
APA, Harvard, Vancouver, ISO, and other styles
3

You, Yuqi. "Positive Affect Promotes Unbiased and Flexible Attention: Towards a Dopaminergic Model of Positivity." Thesis, 2011. http://hdl.handle.net/1807/31666.

Full text
Abstract:
A review of extant literature on positive affect suggested that it has two major dimensions: a hedonic dimension related to subjective feelings and reward processing, and a cognitive dimension related to affect-specific changes in perception and cognition. A novel dopaminergic mod el was proposed to provide a unitary account for the effects of positive affect across the two dimensions. The model hypothesized that positive affect is associated with distinct modes of mesocortical and mesolimbic dopa mine transmission, which in turn mediate unbiased, unfiltered and flexible attention. Three separate behavioral tasks on perception, attention, and reward learning were conducted. In line with the hypothesis, positive affect was found to associate with less biased bi-stable perception, faster regain of attention to previously ignored information, and fewer perseverative errors in face of changing reward contingencies.
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Unbiased Learning"

1

Kitt, Mario. Clear and Unbiased Facts about Learning Italian. Lulu Press, Inc., 2015.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Fregni, Felipe, and Ben M. W. Illigens, eds. Critical Thinking in Clinical Research. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199324491.001.0001.

Full text
Abstract:
Critical Thinking in Clinical Research explains the fundamentals of clinical research in a case-based approach. The core concept is to combine a clear and concise transfer of information and knowledge with an engagement of the reader to develop a mastery of learning and critical thinking skills. The book addresses the main concepts of clinical research, basics of biostatistics, advanced topics in applied biostatistics, and practical aspects of clinical research, with emphasis on clinical relevance across all medical specialties. The goal of the book is to give a comprehensive and basic overview of the field of clinical research. This book has been designed on the experience of leading a large course in clinical research: the Principles and Practice in Clinical Research (PPCR), offered currently by Harvard T. H. Chan School of Public Health; it was written by PPCR collaborators together with PPCR faculty to reflect the collaborative learning concept of the course. The goal of this book is to provide a broad and applicable introduction into clinical research that allows the reader to understand, design, and conduct clinical research, specifically to critically read and understand scientific papers; to collect, analyze, and interpret research data in an unbiased fashion; to develop and design clinical studies; and to prepare, publish, and review scientific manuscripts. It is therefore written for scientists and clinicians who are new to the field of clinical research as well as those who wish to deepen, broaden, and update their clinical research skills.
APA, Harvard, Vancouver, ISO, and other styles
3

Singer, Donald, and W. David Menzie. Quantitative Mineral Resource Assessments. Oxford University Press, 2010. http://dx.doi.org/10.1093/oso/9780195399592.001.0001.

Full text
Abstract:
Policy makers, mineral exploration experts, and regional planners decide how public lands, which may contain undiscovered resources, should be used or whether to invest in exploration for minerals on a regular basis. Decisions are also made concerning mineral resource adequacy, national policy, and regional development. This book makes explicit the factors that can affect a mineral-related decision so that decision-makers can clearly see the possible consequences of their decisions. Based on work done at the US Geological Survey, the authors address the question of the kinds of issues decision-makers are trying to resolve and what forms of information would aid in resolving these issues. The goal of the process discussed is to offer unbiased quantitative assessments in a format needed in decision-support systems so that consequences of alternative courses of action can be examined with respect to land use or mineral-resource development. An integrated approach focuses on three assessment parts and the models that support them. Although the concepts presented are straightforward and understandable, in assessments, carefully listening to the experts in other disciplines leads to better products. Navigating through and making sense of QRA requires not just learning rules and equations, but life experiences and common sense. The judgment required to understand which tools to apply are best learned by example and experience. This will be useful to governmental or industrial policy makers, managers of explorations, planners of regional development, and similar decision-makers.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Unbiased Learning"

1

Grari, Vincent, Oualid El Hajouji, Sylvain Lamprier, and Marcin Detyniecki. "Learning Unbiased Representations via Rényi Minimization." In Machine Learning and Knowledge Discovery in Databases. Research Track, 749–64. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86520-7_46.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Cleophas, Ton J., and Aeilko H. Zwinderman. "Complex Samples Methodologies for Unbiased Sampling (9,678 Persons)." In Machine Learning in Medicine - Cookbook, 85–90. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04181-0_14.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Cleophas, Ton J., and Aeilko H. Zwinderman. "Complex Samples Methodologies for Unbiased Sampling (9,678 Persons)." In Machine Learning in Medicine - a Complete Overview, 313–19. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15195-3_51.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Cleophas, Ton J., and Aeilko H. Zwinderman. "Complex Samples Methodologies for Unbiased Sampling (9678 Persons)." In Machine Learning in Medicine – A Complete Overview, 429–36. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33970-8_56.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Chaudhury, Sreejit, Ajeet Kumar, Subhamoy Maitra, Somjit Roy, and Sourav Sen Gupta. "A Heuristic Framework to Search for Approximate Mutually Unbiased Bases." In Cyber Security, Cryptology, and Machine Learning, 208–23. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07689-3_16.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Hu, Jian, Haowen Zhong, Fei Yang, Shaogang Gong, Guile Wu, and Junchi Yan. "Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modeling." In Lecture Notes in Computer Science, 223–41. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19821-2_13.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Preprocessing Tools for Data Preparation." In Multivariate Statistical Machine Learning Methods for Genomic Prediction, 35–70. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_2.

Full text
Abstract:
AbstractThis data preparation chapter is of paramount importance for implementing statistical machine learning methods for genomic selection. We present the basic linear mixed model that gives rise to BLUE and BLUP and explain how to decide when to use fixed or random effects that give rise to best linear unbiased estimates (BLUE or BLUEs) and best linear unbiased predictors (BLUP or BLUPs). The R codes for fitting linear mixed model for the data are given in small examples. We emphasize tools for computing BLUEs and BLUPs for many linear combinations of interest in genomic-enabled prediction and plant breeding. We present tools for cleaning, imputing, and detecting minor and major allele frequency computation, marker recodification, frequency of heterogeneous, frequency of NAs, and three methods for computing the genomic relationship matrix. In addition, scaling and data compression of inputs are important in statistical machine learning. For a more extensive description of linear mixed models, see Chap. 10.1007/978-3-030-89010-0_5.
APA, Harvard, Vancouver, ISO, and other styles
8

Cheng, Jieyu, Adrian V. Dalca, and Lilla Zöllei. "Unbiased Atlas Construction for Neonatal Cortical Surfaces via Unsupervised Learning." In Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis, 334–42. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60334-2_33.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Zhao, Rongchang, Xuanlin Chen, Zailiang Chen, and Shuo Li. "EGDCL: An Adaptive Curriculum Learning Framework for Unbiased Glaucoma Diagnosis." In Computer Vision – ECCV 2020, 190–205. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58589-1_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Reimers, Christian, Paul Bodesheim, Jakob Runge, and Joachim Denzler. "Conditional Adversarial Debiasing: Towards Learning Unbiased Classifiers from Biased Data." In Lecture Notes in Computer Science, 48–62. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92659-5_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Unbiased Learning"

1

Ai, Qingyao, Keping Bi, Cheng Luo, Jiafeng Guo, and W. Bruce Croft. "Unbiased Learning to Rank with Unbiased Propensity Estimation." In SIGIR '18: The 41st International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3209978.3209986.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Ai, Qingyao, Jiaxin Mao, Yiqun Liu, and W. Bruce Croft. "Unbiased Learning to Rank." In CIKM '18: The 27th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3269206.3274274.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Ai, Qingyao, Jiaxin Mao, Yiqun Liu, and W. Bruce Croft. "Unbiased Learning to Rank." In ICTIR '18: The 2018 ACM SIGIR International Conference on the Theory of Information Retrieval. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3234944.3234980.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Hu, Ziniu, Yang Wang, Qu Peng, and Hang Li. "Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm." In WWW '19: The Web Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3308558.3313447.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Sekino, Masashi, and Katsumi Nitta. "Unbiased Learning for Hierarchical Models." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371020.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Diskin, Tzvi, Yonina C. Eldar, and Ami Wiesel. "Learning Minimum Variance Unbiased Estimators." In 2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2022. http://dx.doi.org/10.1109/sam53842.2022.9827845.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Liang, Jian, Yuren Cao, Chenbin Zhang, Shiyu Chang, Kun Bai, and Zenglin Xu. "Additive Adversarial Learning for Unbiased Authentication." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.01169.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Bo Geng, Linjun Yang, Zheng-Jun Zha, Chao Xu, and Xian-Sheng Hua. "Unbiased active learning for image retrieval." In 2008 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2008. http://dx.doi.org/10.1109/icme.2008.4607687.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Oosterhuis, Harrie, and Maarten de Rijke. "Differentiable Unbiased Online Learning to Rank." In CIKM '18: The 27th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3269206.3271686.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Joachims, Thorsten, Adith Swaminathan, and Tobias Schnabel. "Unbiased Learning-to-Rank with Biased Feedback." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/738.

Full text
Abstract:
Implicit feedback (e.g., clicks, dwell times, etc.) is an abundant source of data in human-interactive systems. While implicit feedback has many advantages (e.g., it is inexpensive to collect, user-centric, and timely), its inherent biases are a key obstacle to its effective use. For example, position bias in search rankings strongly influences how many clicks a result receives, so that directly using click data as a training signal in Learning-to-Rank (LTR) methods yields sub-optimal results. To overcome this bias problem, we present a counterfactual inference framework that provides the theoretical basis for unbiased LTR via Empirical Risk Minimization despite biased data. Using this framework, we derive a propensity-weighted ranking SVM for discriminative learning from implicit feedback, where click models take the role of the propensity estimator. Beyond the theoretical support, we show empirically that the proposed learning method is highly effective in dealing with biases, that it is robust to noise and propensity model mis-specification, and that it scales efficiently. We also demonstrate the real-world applicability of our approach on an operational search engine, where it substantially improves retrieval performance.
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Unbiased Learning"

1

Andrabi, Tahir, Natalie Bau, Jishnu Das, and Asim I. Khwaja. Heterogeneity in School Value-Added and the Private Premium. Research on Improving Systems of Education (RISE), November 2022. http://dx.doi.org/10.35489/bsg-risewp_2022/116.

Full text
Abstract:
Using rich panel data from Pakistan, we compute test score based measures of quality (School Value-Addeds or SVAs) for more than 800 schools across 112 villages and verify that they are valid and unbiased. With the SVA measures, we then document three striking features of the schooling environment. First, there is substantial within-village variation in quality. The annualized difference in learning between the best and worst performing school in the same village is 0.4 sd; compounded over 5 years of primary schooling, this difference is similar in size to the test score gap between low- and high-income countries. Second, students learn more in private schools (0.15 sd per year on average), but substantial within-sector variation in quality means that the effects of reallocating students from public to private schools can range from -0.35sd to +0.65sd. Thus, there is a range of possible causal estimates of the private premium, a feature of the environment we illustrate using three different identification approaches. Finally, parents appear to recognize and reward SVA in the private sector, but the link between parental demand and SVA is weaker in the public sector. These results have implications for both the measurement of the private premium and how we design and evaluate policies that reallocate children across schools, such as school closures and vouchers.
APA, Harvard, Vancouver, ISO, and other styles
2

Adegoke, Damilola, Natasha Chilambo, Adeoti Dipeolu, Ibrahim Machina, Ade Obafemi-Olopade, and Dolapo Yusuf. Public discourses and Engagement on Governance of Covid-19 in Ekiti State, Nigeria. African Leadership Center, King's College London, December 2021. http://dx.doi.org/10.47697/lab.202101.

Full text
Abstract:
Numerous studies have emerged so far on Covid-19 (SARS-CoV-2) across different disciplines. There is virtually no facet of human experience and relationships that have not been studied. In Nigeria, these studies include knowledge and attitude, risk perception, public perception of Covid-19 management, e-learning, palliatives, precautionary behaviours etc.,, Studies have also been carried out on public framing of Covid-19 discourses in Nigeria; these have explored both offline and online messaging and issues from the perspectives of citizens towards government’s policy responses such as palliative distributions, social distancing and lockdown. The investigators of these thematic concerns deployed different methodological tools in their studies. These tools include policy evaluations, content analysis, sentiment analysis, discourse analysis, survey questionnaires, focus group discussions, in depth-interviews as well as machine learning., These studies nearly always focus on the national government policy response, with little or no focus on the constituent states. In many of the studies, the researchers work with newspaper articles for analysis of public opinions while others use social media generated contents such as tweets) as sources for analysis of sentiments and opinions. Although there are others who rely on the use of survey questionnaires and other tools outlined above; the limitations of these approaches necessitated the research plan adopted by this study. Most of the social media users in Nigeria are domiciled in cities and their demography comprises the middle class (socio-economic) who are more likely to be literate with access to internet technologies. Hence, the opinions of a majority of the population who are most likely rural dwellers with limited access to internet technologies are very often excluded. This is not in any way to disparage social media content analysis findings; because the opinions expressed by opinion leaders usually represent the larger subset of opinions prevalent in the society. Analysing public perception using questionnaires is also fraught with its challenges, as well as reliance on newspaper articles. A lot of the newspapers and news media organisations in Nigeria are politically hinged; some of them have active politicians and their associates as their proprietors. Getting unbiased opinions from these sources might be difficult. The news articles are also most likely to reflect and amplify official positions through press releases and interviews which usually privilege elite actors. These gaps motivated this collaboration between Ekiti State Government and the African Leadership Centre at King’s College London to embark on research that will primarily assess public perceptions of government leadership response to Covid-19 in Ekiti State. The timeframe of the study covers the first phase of the pandemic in Ekiti State (March/April to August 2020).
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Unbiased Learning' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography