95704 - ANALYSING CULTURAL CHANGE AND CULTURAL TRANSMISSION: A METHODOLOGICAL PERSPECTIVE

Conoscenze e abilità da conseguire

Human culture is the result of cumulative processes of transmission, innovation, environmental and socio-economic pressure, human migrations, and the exchange of objects and ideas at different spatial and temporal scales. The course focuses on both theoretical and methodological questions raised by current approaches to the study of cultural change, and adds a focus on the basic statistical tools currently used to analyze data generated in the fields of cultural heritage, archaeology, anthropology, and other social sciences. By the end of the course, the student has acquired skills that facilitate the systematic description of empirical evidence and the exploration of diversity in different contexts. At the same time, the student understands the basics of hypothesis testing in the light of broader cultural and anthropological processes, and becomes aware of good and bad practices related to the use of quantitative methods in the social sciences.

Contenuti

The course entails frontal lectures covering theoretical and methodological issues followed by an overview of specific techniques during guided (both individual and group) practical sessions. More in detail, the course will be articulated as follows:

1. Quantitative methods: why bother? Introduction to contents and aim of the course. Overview of the emergence and diffusion of quantitative methods, their first appearance in archaeology and anthropology, biology, sociology and economics; their abandonment due the diffusion of post-modern epistemology; the reinstatement of quantitative knowledge in contemporary research.

2. Uncertainty, equifinality, and underdetermination. All disciplines that aim to understand and explain invisible/fragmentary processes based on visible/accessible patterns have to come to terms with the abundance of plausible scenarios and problems of resolution and scale. How can we effectively deal with them?

3. What is culture? What definitions have been developed and used for it in the past? Which one can we use now? Is it a chiefly human trait? Or is it rather something broader? Is it a working term we can use or should we rather avoid it altogether? How does this reflect on the study of Cultural Heritage? And what about the political/economic use of cultural heritage? The class is actively involved in discussing different points of view and to try to converge (if possible) towards a common ground.

4. The Transmission of Culture, from Evolutionism to Evolution. Thoughs, ideas, and objects clearly undergo a process of change over time and space. Quantitative methods help with identifying traces of such change. Overview of the emergence and development of the Cultural Evolutionist paradigm in Anthropology and Archaeology, its impact on today’s European perspective, and the later shift towards a different paradigm informed by population genetics. Comparison with postmodern approaches, qualitative approaches, and active contrast between written sources in group discussion sessions.

5. Morelli’s ear and Petrie’s vessels. What do the method established by the famous art connoisseur Giovanni Morelli and the seriation of ancient Egyptian ceramics elaborated by archaeologist Flinders Petrie have in common? Introduction to the concepts of unbiased cultural transmission, frequency seriation, the concept of fashion cycles, and their relationship with models developed outside the social sciences.

6. Divide et impera: the importance of controlling your taxonomy. Researchers interested in human culture often have to categorise continuous variables in order to compare them with groups of interest. This endeavour often leads to frustration and forces us to leave the particular for the general. To avoid sinking in the sea of variability generated by such a practice, they have to ordinate their data in ways that are consistent with their own research questions and yet are repeatable and can be tested by others. Overview of the many techniques developed over time for classifying and grouping elements in spite of the noisy variability characterising a dataset.

7. Lost in translation. Terms and symbols used in mathematics and informatics are simply another, more formal type of expression we can learn in order to exchange information with other scientific disciplines. Trying to learn at least the basic grammar can free us from depending on translators, and can help us understanding the potential concealed in our dataset. Overview of available statistical software and languages. Introduction to R, software installation. Introductory practical session.

8. It’s possible. Even better, it’s probable. Introduction to the concept of probability and the importance of expressing concepts in probabilistic terms. Definitions of probability developed by different statistical approaches. Communicating probability, difficulties and possible solutions. The fundamental tenets of probability theory. The concepts of power, base rate, effect size, and p-hacking.

9. Measuring shadows in Plato’s Cave to understand the world outside. Information, uncertainty and models of knowledge. Variables and constants. Scales of measurement. Accuracy. The types of variables which social scientists, archaeologists, anthropologists, and art historians usually have to treat. Descriptive statistics, basic graphical representations (bar charts, histograms, boxplots).

10. We value diversity. Anthropologists, archaeologists, and social scientists are interested in studying change in human culture over time and space. In order to appreciate such change they need to use appropriate measures of diversity that can be easily compared against each other, over time, and across space.

11. What do you mean by normal? The normal distribution, its history, scientific relevance, and implications. Measures of central tendency and dispersion. The importance of visually inspecting a dataset. Some examples of non-normal distributions. Visualization and use of the normal distribution in R.

12. Does the exception prove the rule? We are interested in understanding why some observations do not conform to our expectations. How can we establish what is different or extreme enough to catch our attention? Introduction to hypothesis testing and exploration of the three main approaches to probability. Methods for comparing groups of observations.

13. Correlation ≠ causation. Determining the relationship between variables does not automatically entail causality. In any case, understanding and measuring association between variables is always the first step.

14. The map is not the territory. We can use a compass if we do not want to get lost. There are methods developed to ordinate and synthesize data containing lots of variables, to avoid getting lost in the clouds of their variability. Conceiving and exploring a multi-dimensional space of variation.

15. All models are wrong. To what extent is your one useful? What does it mean to create a model? How can we use it to understand something more about reality? Even just a single line can be a model. Shifting from measuring the relationship between variables to measuring the effect that one variable has on another (i.e. establishing a causal relationship) makes us take a big step forward in our understanding of any process of change. Assumptions of linear regression. Importance and limits of models.

Testi/Bibliografia

The program of the course is the same for both students attending and not attending. Owing to the nature of the course, frequency of the lessons is strongly recommended. However, students who for valid reasons cannot attend the course are invited to contact the teacher, during the office hours, for the suggestion of potential supplementary texts.

Additional teaching material and lecture presentations will be provided during the course.

 

Compulsory readings (parts indicated during the course)

# Levels of measurement
Shennan, S. J., 1997. Quantifying Archaeology (2nd edition). Edimburgh: Edimburgh University Press
- Cap 2 Quantifying Description

# Basic graphs
Shennan, S. J., 1997. Quantifying Archaeology (2nd edition). Edimburgh: Edimburgh University Press
- Cap 3 Picture Summaries of a Single Variable

# Descriptive statistics
Shennan, S. J., 1997. Quantifying Archaeology (2nd edition). Edimburgh: Edimburgh University Press
- Cap 4 Numerical Summaries of a Single Variable

# The normal distribution
Shennan, S. J., 1997. Quantifying Archaeology (2nd edition). Edimburgh: Edimburgh University Press
- Cap 8 Numeric Variables: the Normal Distribution

# Chi-squared test
Shennan, S. J., 1997. Quantifying Archaeology (2nd edition). Edimburgh: Edimburgh University Press
- Cap 6 The Chi-squared test

# The same contents of all chapteras above can be explored in Italian in the volume:
Agresti, A., Franklin, CA. 2016. Statistica: L'arte e la scienza d'imparare dai dati, Pearson.

# Big data and the use of the past in contemporary politcal debate
Bonacchi, C. 2022. Heritage and Nationalism: Understanding populism through big data, UCL Press (free download from https://library.oapen.org/handle/20.500.12657/57798), Chapters 2, 3, and 7

# Review on cultural evolution
Alex Mesoudi (2020). The study of culture and evolution across disciplines. In Lance Workman, William Reader and Jerome H. Barkow (ed.) Cambridge handbook of evolutionary perspectives on human behavior. Cambridge University Press, pp. 61–74 (preprint accessible from https://alexmesoudi.com/files/papers/Mesoudi_CamHandbook_2020.pdf)

# Limits and perils: the misuse of quantitative methods
Rohrer, J. M., Schmukle, S., & McElreath, R. (2021). The Only Thing That Can Stop Bad Causal Inference Is Good Causal Inference. (https://psyarxiv.com/mz5jx)

Smaldino PE, McElreath R (2016) The natural selection of bad science. Royal Society Open Science, 3, 160384

Smaldino PE (2019) Better methods can’t make up for mediocre theory. Nature 575: 9

Wang, Lihshing Leigh and others, ‘Common Fallacies in Quantitative Research Methodology’, in Todd D. Little (ed.), The Oxford Handbook of Quantitative Methods in Psychology: Vol. 2: Statistical Analysis, Oxford Library of Psychology (2013; online edn, Oxford Academic, 1 Oct. 2013), https://doi.org/10.1093/oxfordhb/9780199934898.013.0031


** In addition, for students who do not attend classes, two to be chosen among: **

Shennan, S. J., 1997. Quantifying Archaeology (2nd edition). Edimburgh: Edimburgh University Press.

Carlson, D.L. 2017. Quantitative Methods in Archaeology Using R, Cambridge, Cambridge University Press - Part I, chapters 1-9

Agresti, A., Franklin, CA. 2016. Statistica: L'arte e la scienza d'imparare dai dati, Pearson.

Moore D.S., Fligner M.A.,Notz W.I. 2021. The basic practice of statistics, New York:Macmillan International


Suggested readings

#Manuals
Borra S., Di Ciaccio A. 2021. Statistica. Metodologie per le scienze economiche e sociali, McGraw-Hill.

Fletcher, M. and Lock, G. 2005. Digging numbers (2nd edition), Oxford, Oxford University School of Archaeology

Madrigal, L. 2012 (2nd edition) Statistics for Anthropology, Cambridge, Cambridge University Press

Mineo, A.M. 2003. Una Guida all’Utilizzo dell’Ambiente Statistico R, disponibile al link https://cran.r-project.org/doc/contrib/Mineo-dispensaR.pdf


#Papers, chapters, and other books
Reinhard, A. 2015. Statistics done wrong. The woefully complete guide, No Starch Press

Acerbi A, Burns J, Cabuk U, Kryczka J, Trapp B, Valletta JJ, Mesoudi A (2023), Sentiment analysis of the Twitter in response to Netflix’s Our Planet documentary, Conservation Biology, in press

Barbieri, C., Blasi, D. E., Arango-Isaza, E., Sotiropoulos, A. G., Hammarström, H., Wichmann, S., Greenhill, S. J., Gray, R. D., Forkel, R., Bickel, B., & Shimizu, K. K. 2022. A global analysis of matches and mismatches between human genetic and linguistic histories. Proceedings of the National Academy of Sciences, 119(47), e2122084119 https://www.pnas.org/doi/10.1073/pnas.2122084119

Carl T. Bergstrom and Jevin West, 2021. Calling Bullshit: the Art of Skepticism in a Data-Driven World, Penguin Random House (https://www.callingbullshit.org/)

Bonacchi C. and Krzyzanska M. 2021. Heritage-based tribalism in big data ecologies: deploying origin myths for antagonistic othering, Big Data & Society, 10.1177/20539517211003310

Dunnell, R. C., 1971. Systematics in Prehistory. Caldwell, New Jersey: The Blackburn Press.

Houle, C., D.J. Ruck, R.A. Bentley, S. Gavrilets (2022). Inequality between identity groups and social unrest. J. R. Soc. Interface 19: 20210725.

Legendre, P. and Legendre, L., 1998. Numerical Ecology (Second English Edition). Elsevier.

Leonard, R. D. and Jones, G. T. (eds.), 1989. Quantifying Diversity in Archaeology. Cambridge University Press: Cambridge.

Lyman, R. L. and OBrien, M. J., 2006. Measuring Time with Artifacts: A History of Methods in American Archaeology. Lincoln: University of Nebraska Press.

Alex Mesoudi (2021). What is cumulative culture, and how should it be tested? A comment on Vaesen, K. & Houkes, W. Is human culture cumulative?. Current Anthropology 62, 225-226. https://doi.org/10.1086/714032.

Alex Mesoudi (2018). Migration, acculturation, and the maintenance of between-group cultural variation. PLOS ONE 13, e0205573. https://doi.org/10.1371/journal.pone.0205573

Alex Mesoudi (2011). Cultural evolution: How Darwinian theory can explain human culture and synthesize the social sciences. University of Chicago Press

Reinhart A. 2015. Statistics Done Wrong: The Woefully Complete Guide, No Starch Press (https://www.statisticsdonewrong.com/)

Ruck, D.J. L.J. Matthews, T. Kyritsis, Q.D. Atkinson, R.A. Bentley (2020). The cultural foundations of modern democracies. Nature Human Behaviour 4: 265–269.

Smaldino PE (2020) How to translate a verbal theory into a formal model. Social Psychology 51: 207–218

Smaldino PE (2017) Models are stupid, and we need more of them. In RR Vallacher, A Nowak, & SJ Read (Eds.), Computational social psychology. Psychology Press

Yongqi, L., Ruixia, Y., Pu, W. et al. A quantitative description of the spatial–temporal distribution and evolution pattern of world cultural heritage. Herit Sci 9, 80 (2021). https://doi.org/10.1186/s40494-021-00549-6

Zhang, H., & Mace, R. (2021). Cultural extinction in evolutionary perspective. Evolutionary Human Sciences, 3, E30. doi:10.1017/ehs.2021.25

Zhang, J., Xiong, K., Liu, Z. et al. Research progress and knowledge system of world heritage tourism: a bibliometric analysis. Herit Sci 10, 42 (2022). https://doi.org/10.1186/s40494-022-00654-0

Metodi didattici

Frontal lectures

Individual practical sessions and presentations / group practical sessions and presentations

Discussion on selected papers

Language: English

Modalità di verifica e valutazione dell'apprendimento

Students who are not able to attend classes will be evaluated in the same way as those who will attend. In case of doubts or questions students can directly contact the course coordinator for clarifications. Details on datasets, essay structure, and research questions for examination will be offered during classes and contained in the teaching materials available on the online web page of the course.

Evaluation for this course will consist of two steps:

1. Submission of a short essay (2000 words max and at least two figures and one tables) structured as a research article for scientific journals, presenting the results of a review or analyses carried out on a given dataset to answer given research questions. Missing or wrong answers will be penalised with a subtraction of max 2 points per research question from a maximum total score of 30 points. The essay will provide evidence of successful acquisition of concepts and skills linked to the effective presentation of data, methods, and results (50% or 70% of final grade depending on the type of essay chosen by the student). Submission of essays by the appropriate deadline (detailed on AlmaEsami, usually ~7 days before the date of the oral examination) is mandatory to enroll for the following oral examination.

2. Oral examination (monthly exam dates are published each semester) in which the student is required to discuss the essay and to answer questions emerged on its content. Other themes and concepts explained during the course will also be discussed through specific questions on quantitative methods (50% or 30% of final grade depending on the type of essay chosen by the student). This second examination will confirm or change (either positively or negatively) the evaluation of the essay.

The final grade will be based on the level of methodological knowledge acquired during the course, on the accuracy with which instructions on formatting and essay structure have been followed, on the degree of independence expressed by the interpretation of results in light of available data and research questions.

The ability of the student to achieve a coherent and comprehensive understanding of the topics addressed by the course, to critically assess them and to use an appropriate language will be evaluated with the highest grades (A = 27-30). Innovative/independent/critical thinking, the search for independent solutions, and creativity in interpreting results will result in the addition of a distinction (30 cum Laude). A predominantly mnemonic acquisition of the course's contents together with gaps and deficiencies in terms of language, critical and/or logical skills will result in grades ranging from good (B = 24-26) to satisfactory (C = 21-23). A low level of knowledge of the course’s contents together with gaps and deficiencies in terms of language, critical and/or logical skills will be considered as ‘barely passing' (D = 18-20) or result in a fail grading (F).

 

 

Strumenti a supporto della didattica

Additional teaching material and lecture presentations will be available on the online course web page.

 Students with a form of disability or specific learning disabilities (DSA) who are requesting academic adjustments or compensatory tools are invited to communicate their needs to the teaching staff in order to properly address them and agree on the appropriate measures with the competent bodies.

Orario di ricevimento

Consulta il sito web di Eugenio Bortolini