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Social Science

From Multiracial to Monoracial: The Formation of Mexican American Identities in the U.S. Southwest

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G. Reginald Daniel

G. Reginald Daniel

Department of Sociology, University of California,

rdaniel@soc.ucsb.edu

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  Peer Reviewed

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© attribution CC-BY

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1926 Views

Added on

2022-07-02

Doi: https://doi.org/10.3390/genealogy6020028

Related Subjects
Law
Politics
Economics
Geography
Education
Sociology

Abstract

The last decade saw a rapid increase in the number of studies where time–frequency changes of radiocarbon dates have been used as a proxy for inferring past population dynamics. Although its universal and straightforward premise is appealing and undoubtedly offers some unique opportunities for research on long-term comparative demography, practical applications are far from trivial and riddled with issues pertaining to the very nature of the proxy under examination. Here I review the most common criticisms concerning the nature of radiocarbon time–frequency data as a demographic proxy, focusing on key statistical and inferential challenges. I then examine and compare recent methodological advances in the field by grouping them into three approaches: reconstructive, null-hypothesis significance testing, and model fitting. I will then conclude with some general recommendations for applying these techniques in archaeological and paleo-demographic research.

Key Questions

What is the main focus of the article on radiocarbon time–frequency data and population dynamics?

The article focuses on using radiocarbon time–frequency data as a proxy for inferring past population dynamics. It reviews challenges, methodological advances, and provides recommendations for applying these techniques in archaeological and paleo-demographic research.

Why is radiocarbon time–frequency data used as a demographic proxy in archaeology?

Radiocarbon time–frequency data is used as a demographic proxy because it provides a universal and straightforward way to study long-term population trends. Changes in the frequency of radiocarbon dates are assumed to reflect changes in human activity and population size.

What are the key challenges of using radiocarbon data to infer past population dynamics?

Key challenges include sampling biases, taphonomic processes, calibration effects, and statistical and inferential difficulties when interpreting the data to infer past population changes.

What are the three methodological approaches for analyzing radiocarbon time–frequency data?

The three approaches are reconstructive (building population models), null-hypothesis significance testing (testing specific hypotheses), and model fitting (fitting statistical models to infer demographic trends).

How does the reconstructive approach work in paleo-demographic research?

The reconstructive approach involves building population models based on radiocarbon data. It analyzes changes in the frequency of radiocarbon dates over time, using statistical techniques to account for biases and uncertainties.

What is the purpose of null-hypothesis significance testing in radiocarbon-based demographic studies?

Null-hypothesis significance testing evaluates specific hypotheses about past population changes by testing whether observed patterns in radiocarbon data deviate from expected patterns under a null hypothesis.

How does model fitting improve inferences about past population dynamics?

Model fitting applies statistical models to radiocarbon data to infer demographic trends. It accounts for uncertainties and biases, providing more robust estimates of past population dynamics.

What are the general recommendations for using radiocarbon data in demographic research?

The article recommends addressing limitations and biases in radiocarbon data, using multiple methodological approaches to cross-validate results, and integrating radiocarbon data with other archaeological and environmental proxies.

How does the article contribute to archaeological and paleo-demographic research?

The article provides a comprehensive review of challenges and methodological advances in using radiocarbon data as a demographic proxy. It offers practical recommendations to improve the accuracy and reliability of demographic inferences.

What are the broader implications of using radiocarbon time–frequency data for studying population dynamics?

The article highlights the potential of radiocarbon data for studying long-term demographic trends but emphasizes the need for rigorous methods to address its limitations. This has implications for understanding human-environment interactions, cultural evolution, and population change in the past.

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2024 December 55 55
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2024 October 124 124
2024 September 85 85
2024 August 39 39
2024 July 54 54
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2024 April 56 56
2024 March 48 48
2024 February 35 35
2024 January 41 41
2023 December 48 48
2023 November 52 52
2023 October 32 32
2023 September 32 32
2023 August 21 21
2023 July 43 43
2023 June 35 35
2023 May 40 40
2023 April 40 40
2023 March 44 44
2023 February 2 2
2023 January 5 5
2022 December 27 27
2022 November 60 60
2022 October 28 28
2022 September 34 34
2022 August 69 69
2022 July 55 55
Total 1926 1926
Show by month Manuscript Video Summary
2025 May 186 186
2025 April 109 109
2025 March 95 95
2025 February 73 73
2025 January 101 101
2024 December 55 55
2024 November 78 78
2024 October 124 124
2024 September 85 85
2024 August 39 39
2024 July 54 54
2024 June 40 40
2024 May 40 40
2024 April 56 56
2024 March 48 48
2024 February 35 35
2024 January 41 41
2023 December 48 48
2023 November 52 52
2023 October 32 32
2023 September 32 32
2023 August 21 21
2023 July 43 43
2023 June 35 35
2023 May 40 40
2023 April 40 40
2023 March 44 44
2023 February 2 2
2023 January 5 5
2022 December 27 27
2022 November 60 60
2022 October 28 28
2022 September 34 34
2022 August 69 69
2022 July 55 55
Total 1926 1926
Related Subjects
Law
Politics
Economics
Geography
Education
Sociology
copyright icon

© attribution CC-BY

  • 0

rating
1926 Views

Added on

2022-07-02

Doi: https://doi.org/10.3390/genealogy6020028

Related Subjects
Law
Politics
Economics
Geography
Education
Sociology

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