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1. PageRank and the Bradley-Terry model

Author

Selby, David Antony

Subjects
Statistics - Methodology
Abstract

PageRank and the Bradley-Terry model are competing approaches to ranking entities such as teams in sports tournaments or journals in citation networks. The Bradley-Terry model is a classical statistical method for ranking based on paired comparisons. The PageRank algorithm ranks nodes according to their importance in a network. Whereas Bradley-Terry scores are computed via maximum likelihood estimation, PageRanks are derived from the stationary distribution of a Markov chain. More recent work has shown maximum likelihood estimates for the Bradley-Terry model may be approximated from such a limiting distribution, an interesting connection that has been discovered and rediscovered over the decades. Here we show - through relatively simple mathematics - a connection between paired comparisons and PageRank that exploits the quasi-symmetry property of the Bradley-Terry model. This motivates a novel interpretation of Bradley-Terry scores as 'scaled' PageRanks, and vice versa, with direct implications for citation-based journal ranking metrics.

Published
2024

2. Quantitative knowledge retrieval from large language models

Author

Selby, David, Spriestersbach, Kai, Iwashita, Yuichiro, Bappert, Dennis, Warrier, Archana, Mukherjee, Sumantrak, Asim, Muhammad Nabeel, Kise, Koichi, and Vollmer, Sebastian

Subjects
Computer Science - Information Retrieval, Computer Science - Computation and Language, Statistics - Applications
Abstract

Large language models (LLMs) have been extensively studied for their abilities to generate convincing natural language sequences, however their utility for quantitative information retrieval is less well understood. In this paper we explore the feasibility of LLMs as a mechanism for quantitative knowledge retrieval to aid data analysis tasks such as elicitation of prior distributions for Bayesian models and imputation of missing data. We present a prompt engineering framework, treating an LLM as an interface to a latent space of scientific literature, comparing responses in different contexts and domains against more established approaches. Implications and challenges of using LLMs as 'experts' are discussed., Comment: 13 pages plus supplementary materials

Published
2024

3. X Hacking: The Threat of Misguided AutoML

Author

Sharma, Rahul, Redyuk, Sergey, Mukherjee, Sumantrak, Sipka, Andrea, Vollmer, Sebastian, and Selby, David

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security
Abstract

Explainable AI (XAI) and interpretable machine learning methods help to build trust in model predictions and derived insights, yet also present a perverse incentive for analysts to manipulate XAI metrics to support pre-specified conclusions. This paper introduces the concept of X-hacking, a form of p-hacking applied to XAI metrics such as Shap values. We show how an automated machine learning pipeline can be used to search for 'defensible' models that produce a desired explanation while maintaining superior predictive performance to a common baseline. We formulate the trade-off between explanation and accuracy as a multi-objective optimization problem and illustrate the feasibility and severity of X-hacking empirically on familiar real-world datasets. Finally, we suggest possible methods for detection and prevention, and discuss ethical implications for the credibility and reproducibility of XAI research., Comment: 13 pages, 8 figures, plus supplementary materials

Published
2024

5. Recruitment and Ongoing Engagement in a UK Smartphone Study Examining the Association Between Weather and Pain: Cohort Study

Author

Druce, Katie L, McBeth, John, van der Veer, Sabine N, Selby, David A, Vidgen, Bertie, Georgatzis, Konstantinos, Hellman, Bruce, Lakshminarayana, Rashmi, Chowdhury, Afiqul, Schultz, David M, Sanders, Caroline, Sergeant, Jamie C, and Dixon, William G

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundThe huge increase in smartphone use heralds an enormous opportunity for epidemiology research, but there is limited evidence regarding long-term engagement and attrition in mobile health (mHealth) studies. ObjectiveThe objective of this study was to examine how representative the Cloudy with a Chance of Pain study population is of wider chronic-pain populations and to explore patterns of engagement among participants during the first 6 months of the study. MethodsParticipants in the United Kingdom who had chronic pain (≥3 months) and enrolled between January 20, 2016 and January 29, 2016 were eligible if they were aged ≥17 years and used the study app to report any of 10 pain-related symptoms during the study period. Participant characteristics were compared with data from the Health Survey for England (HSE) 2011. Distinct clusters of engagement over time were determined using first-order hidden Markov models, and participant characteristics were compared between the clusters. ResultsCompared with the data from the HSE, our sample comprised a higher proportion of women (80.51%, 5129/6370 vs 55.61%, 4782/8599) and fewer persons at the extremes of age (16-34 and 75+). Four clusters of engagement were identified: high (13.60%, 865/6370), moderate (21.76%, 1384/6370), low (39.35%, 2503/6370), and tourists (25.44%, 1618/6370), between which median days of data entry ranged from 1 (interquartile range; IQR: 1-1; tourist) to 149 (124-163; high). Those in the high-engagement cluster were typically older, whereas those in the tourist cluster were mostly male. Few other differences distinguished the clusters. ConclusionsCloudy with a Chance of Pain demonstrates a rapid and successful recruitment of a large, representative, and engaged sample of people with chronic pain and provides strong evidence to suggest that smartphones could provide a viable alternative to traditional data collection methods.

Published
2017
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6. Judging a book by its cover: how much of REF `research quality' is really `journal prestige'?

Author

Selby, David Antony and Firth, David

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

The Research Excellence Framework (REF) is a periodic UK-wide assessment of the quality of published research in universities. The most recent REF was in 2014, and the next will be in 2021. The published results of REF2014 include a categorical `quality profile' for each unit of assessment (typically a university department), reporting what percentage of the unit's REF-submitted research outputs were assessed as being at each of four quality levels (labelled 4*, 3*, 2* and 1*). Also in the public domain are the original submissions made to REF2014, which include -- for each unit of assessment -- publication details of the REF-submitted research outputs. In this work, we address the question: to what extent can a REF quality profile for research outputs be attributed to the journals in which (most of) those outputs were published? The data are the published submissions and results from REF2014. The main statistical challenge comes from the fact that REF quality profiles are available only at the aggregated level of whole units of assessment: the REF panel's assessment of each individual research output is not made public. Our research question is thus an `ecological inference' problem, which demands special care in model formulation and methodology. The analysis is based on logit models in which journal-specific parameters are regularized via prior `pseudo-data'. We develop a lack-of-fit measure for the extent to which REF scores appear to depend on publication venues rather than research quality or institution-level differences. Results are presented for several research fields., Comment: 50 pages, 19 figures

Published
2023

11. Statistical modelling of citation networks, research influence and journal prestige

Author

Selby, David Antony

Subjects
QA Mathematics, ZA Information resources
Abstract

Standard approaches to measurement of the 'impact' of academic journals, or even sometimes of individual researchers or single research outputs, are typically not based on principled statistical methods for the analysis of citation data, through appropriate statistical models. Recent research has shown the value of such statistical modelling, for citations within a research discipline, for example in reproducing more faithfully the quality judgements of human assessors. In this project we study the strengths and weaknesses of statistical modelling approaches to citation-network data, and in so doing, uncover a deep theoretical connection between two otherwise unrelated journal ranking methods: PageRank and the Bradley-Terry model. We extend the usual journal- or author-based metrics, by aggregating all publications in a given field into 'super-journals'. This permits modelling the exchange of citations between disciplines, raising the question: which scientific fields export the most intellectual influence, through recent research, to other fields? The relative merits of human and algorithmic field classifications are discussed. For this task, we propose a methodology of residual diagnosis for network community structures. Finally, we investigate the extent to which the 2014 Research Excellence Framework's assessment of 'quality' of research outputs (rated 4*, 3*, 2* or 1*) was associated with the reputation of the journals in which those outputs were published. Submissions data are available, as are the aggregate scores for each university department, but the individual ratings for each paper are not. The research question is thus an 'ecological inference' problem attempting to estimate individual-level characteristics from aggregate data. Results are presented for several research fields. To promote reproducibility and enable future research, the thesis includes a vignette on how to obtain citation network data from various databases, and is accompanied by R packages scrooge and ref2014 to facilitate analysis.

Published
2020

14. Education for Sustainable Development, Nature and Vernacular Learning

Author

Selby, David

Abstract

Mainstream education for sustainable development conceives of nature as a resource or commodity. The natural world is, for the most part, accorded only instrumental or utilitarian value. As a field it thus aligns itself with a longstanding paradigm in western thinking that sees humans as separate from and dominant over nature. The de-natured nature of education for sustainable development makes it unlikely that the learner will become motivated to care and act for nature. As an alternative, vernacular learning is proposed, i.e. place-based learning rooted in close intimacy and connection with the natural world, with nature perceived as being intrinsically valuable. The importance of fostering emotional affinity with nature is underlined, as are forms of multi-sensory learning that help the learner engage with both spirit and soul of place. Practical examples of vernacular learning activities are enumerated. The importance of nurturing a sense of wonder and joy in the young learner is put forward as vital in fostering an ethic of concern for the planet. Essentially, the argument goes, we only stir ourselves to protect what we have come to love, and thus cultivating a sense of oneness with nature is vital if we are to have any chance of transforming the global environmental condition. Passion is the harbinger of activism.

Published
2017

20. COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

Author

Abbasizanjani, Hoda, Ahmed, Nida, Ahmed, Badar, Akbari, Ashley, Akinoso-Imran, Abdul Qadr, Allara, Elias, Allery, Freya, Angelantonio, Emanuele Di, Ashworth, Mark, Ayyar-Gupta, Vandana, Babu-Narayan, Sonya, Bacon, Seb, Ball, Steve, Banerjee, Ami, Barber, Mark, Barrett, Jessica, Bennie, Marion, Berry, Colin, Beveridge, Jennifer, Birney, Ewan, Bojanić, Lana, Bolton, Thomas, Bone, Anna, Boyle, Jon, Braithwaite, Tasanee, Bray, Ben, Briffa, Norman, Brind, David, Brown, Katherine, Buch, Maya, Canoy, Dexter, Caputo, Massimo, Carragher, Raymond, Carson, Alan, Cezard, Genevieve, Chang, Jen-Yu Amy, Cheema, Kate, Chin, Richard, Chudasama, Yogini, Cooper, Jennifer, Copland, Emma, Crallan, Rebecca, Cripps, Rachel, Cromwell, David, Curcin, Vasa, Curry, Gwenetta, Dale, Caroline, Danesh, John, Das-Munshi, Jayati, Dashtban, Ashkan, Davies, Alun, Davies, Joanna, Davies, Gareth, Davies, Neil, Day, Joshua, Delmestri, Antonella, Denaxas, Spiros, Denholm, Rachel, Dennis, John, Denniston, Alastair, Deo, Salil, Dhillon, Baljean, Docherty, Annemarie, Dong, Tim, Douiri, Abdel, Downs, Johnny, Dregan, Alexandru, Ellins, Elizabeth A, Elwenspoek, Martha, Falck, Fabian, Falter, Florian, Fan, Yat Yi, Firth, Joseph, Fraser, Lorna, Friebel, Rocco, Gavrieli, Amir, Gerstung, Moritz, Gilbert, Ruth, Gillies, Clare, Glickman, Myer, Goldacre, Ben, Goldacre, Raph, Greaves, Felix, Green, Mark, Grieco, Luca, Griffiths, Rowena, Gurdasani, Deepti, Halcox, Julian, Hall, Nick, Hama, Tuankasfee, Handy, Alex, Hansell, Anna, Hardelid, Pia, Hardy, Flavien, Harris, Daniel, Harrison, Camille, Harron, Katie, Hassaine, Abdelaali, Hassan, Lamiece, Healey, Russell, Hemingway, Harry, Henderson, Angela, Herz, Naomi, Heyl, Johannes, Hidajat, Mira, Higginson, Irene, Hinchliffe, Rosie, Hippisley-Cox, Julia, Ho, Frederick, Hocaoglu, Mevhibe, Hollings, Sam, Horne, Elsie, Hughes, David, Humberstone, Ben, Inouye, Mike, Ip, Samantha, Islam, Nazrul, Jackson, Caroline, Jenkins, David, Jiang, Xiyun, Johnson, Shane, Kadam, Umesh, Kallis, Costas, Karim, Zainab, Kasan, Jake, Katsoulis, Michalis, Kavanagh, Kim, Kee, Frank, Keene, Spencer, Kent, Seamus, Khalid, Sara, Khawaja, Anthony, Khunti, Kamlesh, Killick, Richard, Kinnear, Deborah, Knight, Rochelle, Kolamunnage-Dona, Ruwanthi, Kontopantelis, Evan, Kurdi, Amanj, Lacey, Ben, Lai, Alvina, Lambarth, Andrew, Larzjan, Milad Nazarzadeh, Lawler, Deborah, Lawrence, Thomas, Lawson, Claire, Li, Qiuju, Li, Ken, Llinares, Miguel Bernabeu, Lorgelly, Paula, Lowe, Deborah, Lyons, Jane, Lyons, Ronan, Machado, Pedro, Macleod, Mary Joan, Macleod, John, Malgapo, Evaleen, Mamas, Mamas, Mamouei, Mohammad, Manohar, Sinduja, Mapeta, Rutendo, Martelli, Javiera Leniz, Martos, David Moreno, Mateen, Bilal, McCarthy, Aoife, Melville, Craig, Milton, Rebecca, Mizani, Mehrdad, Moncusi, Marta Pineda, Morales, Daniel, Mordi, Ify, Morrice, Lynn, Morris, Carole, Morris, Eva, Mu, Yi, Mueller, Tanja, Murdock, Lars, Nafilyan, Vahé, Nicholson, George, Nikiphorou, Elena, Nolan, John, Norris, Tom, Norris, Ruth, North, Laura, North, Teri-Louise, O'Connell, Dan, Oliver, Dominic, Oluyase, Adejoke, Olvera-Barrios, Abraham, Omigie, Efosa, Onida, Sarah, Padmanabhan, Sandosh, Palmer, Tom, Pasea, Laura, Patel, Riyaz, Payne, Rupert, Pell, Jill, Petitjean, Carmen, Pherwani, Arun, Pickrell, Owen, Pierotti, Livia, Pirmohamed, Munir, Priedon, Rouven, Prieto-Alhambra, Dani, Proudfoot, Alastair, Quinn, Terry, Quint, Jennifer, Raffetti, Elena, Rahimi, Kazem, Rao, Shishir, Razieh, Cameron, Roberts, Brian, Rogers, Caroline, Rossdale, Jennifer, Salim, Safa, Samani, Nilesh, Sattar, Naveed, Schnier, Christian, Schwartz, Roy, Selby, David, Seminog, Olena, Shabnam, Sharmin, Shah, Ajay, Shelton, Jon, Sheppard, James, Sinha, Shubhra, Skrypak, Mirek, Slapkova, Martina, Sleeman, Katherine, Smith, Craig, Sofat, Reecha, Sosenko, Filip, Sperrin, Matthew, Steeg, Sarah, Sterne, Jonathan, Stoica, Serban, Sudell, Maria, Sudlow, Cathie, Sun, Luanluan, Suseeladevi, Arun Karthikeyan, Sweeting, Michael, Sydes, Matt, Takhar, Rohan, Tang, Howard, Thygesen, Johan, Tilston, George, Tochel, Claire, Toit, Clea du, Tomlinson, Christopher, Toms, Renin, Torabi, Fatemeh, Torralbo, Ana, Townson, Julia, Tufail, Adnan, Tungamirai, Tapiwa, Varma, Susheel, Vollmer, Sebastian, Walker, Venexia, Wang, Tianxiao, Wang, Huan, Warwick, Alasdair, Watkinson, Ruth, Watson, Harry, Whiteley, William, Whittaker, Hannah, Wilde, Harry, Wilkinson, Tim, Williams, Gareth, Williams, Michelle, Williams, Richard, Withnell, Eloise, Wolfe, Charles, Wood, Angela, Wright, Lucy, Wu, Honghan, Wu, Jinge, Wu, Jianhua, Yates, Tom, Zaccardi, Francesco, Zhang, Haoting, Zhang, Huayu, Zuccolo, Luisa, Thygesen, Johan H, Mizani, Mehrdad A, Banerjee, Amitava, Lai, Alvina G, Li, Kezhi, Mateen, Bilal A, Sterne, Jonathan A C, Pagel, Christina, and Whiteley, William N

Published
2022
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22. Multi‐proxy palaeoenvironmental reconstruction of the Skagerrak from the Lateglacial to Middle Holocene.

Author

Ownsworth, Emma, Moros, Matthias, Lloyd, Jeremy, Bennike, Ole, Jensen, Jørn Bo, Blanz, Thomas, and Selby, David

Subjects
HOLOCENE Epoch, X-ray fluorescence, RADIOCARBON dating, GRAIN size, ACCELERATOR mass spectrometry, FORAMINIFERA, FOSSIL microorganisms
Abstract

This study uses a multiproxy approach including the first use of 187Os/188Os, %C37:4 biomarkers, carbonate content, sedimentological grain size, geochemical X‐ray fluorescence and microfossil benthic foraminifera species combined with radiocarbon dating, measured on six cores from across the Skagerrak, in order to study the Lateglacial to Middle Holocene history of the area. A new chronostratigraphic framework is developed based on the appearance of specific benthic foraminifera species along with changes in carbonate/X‐ray fluorescence and grain size data. This allowed the correlation of cores based on a series of radiocarbon dated tie points. Analysing the cores together reveals several events recorded in the Skagerrak including: (i) an increased freshwater input (bracketed between 13.3 and 11.3 cal. ka BP) signified by radiogenic 187Os/188Os values, high %C37:4 values and an increase in sand content; (ii) the Glomma drainage event, signified by a sudden appearance of Valvulineria as well as higher %C37:4; and (iii) the opening of the Danish Straits and English Channel leading to the development of modern‐day conditions and circulation patterns in the Skagerrak, signified by the appearance of Hyalinea balthica and a fall in %C37:4. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Teetering on the Brink: Subversive and Restorative Learning in Times of Climate Turmoil and Disaster

Author

Selby, David and Kagawa, Fumiyo

Abstract

Unchecked climate change poses a self-inflicted existential risk to humanity as it exacerbates the multiple-crisis syndrome facing global society. In international policy, education for sustainable development is widely flagged as transformative. To realize that transformative potential, sustainability educators are exploring the nexus between their field and that of transformative learning. They particularly call for a stretching of epistemology so that unsustainable practices are challenged, taken-for-granted thinking and assumptions disrupted, root causes of global dysfunction interrogated, values subjected to critical scrutiny, change potential of socio-affective learning unleashed, and paradigm shift thus catalyzed. The problem is the overall lack of consensus about what needs sustaining and what needs transforming. Seeking to address that problem, we call for subversive learning interrogating four key climate change drivers: economic growth, consumerism, denial, and climate injustice. We also call for restorative learning in three important areas: restoring nature intimacy, confronting despair, and reclaiming the good life.

Published
2018
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37. Protracted metallogenic and magmatic evolution of the Kirazlı epithermal Au-Ag and porphyry Cu deposits, Biga Peninsula, NW Turkey: evidence from zircon U-Pb, muscovite 40Ar/39Ar, and molybdenite Re-Os geochronology.

Author

Aluç, Ali, Kuşcu, İlkay, Ulyanov, Alexey, Selby, David, Antoine, Clémentine, Spikings, Richard, and Moritz, Robert

Subjects
METALLOGENY, GEOLOGICAL time scales, COPPER, MUSCOVITE, PORPHYRY, MOLYBDENITE
Abstract

The Kirazlı deposit is located at the center of the Biga Peninsula metallogenic province, in a geological setting characterized by an extensional tectonic environment. A NNW-SSE trending high-sulfidation (HS) orebody with a total reserve of 33.86 Mt @ 0.69 g/t Au and 9.42 g/t Ag lies beneath the Kirazlı Main zone. A porphyry Cu orebody hosted by Eocene intrusive and volcanic rocks has been intersected by drilling within its vicinity. The HS epithermal deposit is hosted by a partly silicified and brecciated Oligocene volcanic and volcaniclastic sequence consisting mainly of basaltic andesite lava flow and lithic/crystal tuff. Lithogeochemistry and zircon U-Pb radiometric ages allow us to distinguish three distinct high-K calc-alkaline magmatic events at ca. 41, 38, and 32 Ma, sourced by metasomatized mantle melts, which have interacted with the crust during their ascent. Porphyry Cu mineralization took place at 36.7 ± 0.4 Ma (muscovite 40Ar/39Ar age) with subsequent re-opening and base metal deposition. Crosscutting quartz-pyrite-molybdenite veins were emplaced at 33.6 ± 0.2 Ma (molybdenite Re-Os age), and followed by the HS epithermal Au-Ag event at ca. 31 Ma, based on a previous study. Our radiometric data indicate that the Kirazlı deposit has recorded a long-lasting Cenozoic magmatic and metallogenic evolution during about 10 Myr. Our study demonstrates that successive, independent, and overprinting, but genetically unrelated, HS epithermal precious metal, hydrothermal Mo, base metal, and porphyry Cu systems have been active at the same location during protracted extensional tectonics of the Biga Peninsula. [ABSTRACT FROM AUTHOR]

Published
2024
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