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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: How to Cautiously Uncover the `Black Box' of Machine Learning Models for Legislative Scholars |
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Identification Number: |
doi:10.7910/DVN/I9LSZZ |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2022-01-28 |
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Version: |
1 |
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Bibliographic Citation: |
Jordan, Soren, 2022, "Replication Data for: How to Cautiously Uncover the `Black Box' of Machine Learning Models for Legislative Scholars", https://doi.org/10.7910/DVN/I9LSZZ, Harvard Dataverse, V1 |
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Citation |
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Title: |
Replication Data for: How to Cautiously Uncover the `Black Box' of Machine Learning Models for Legislative Scholars |
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Identification Number: |
doi:10.7910/DVN/I9LSZZ |
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Authoring Entity: |
Jordan, Soren (Auburn University) |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Jordan, Soren |
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Depositor: |
Jordan, Soren |
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Date of Deposit: |
2022-01-27 |
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Holdings Information: |
https://doi.org/10.7910/DVN/I9LSZZ |
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Study Scope |
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Keywords: |
Social Sciences, Social Sciences |
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Abstract: |
This includes the data and scripts necessary to produce the figures and analysis. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
CC0 Waiver |
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Other Study Description Materials |
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Label: |
JPP-LSQ-2022-HO.R |
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Notes: |
type/x-r-syntax |
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Label: |
JPP-LSQ-2022-PR.R |
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Notes: |
type/x-r-syntax |
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Label: |
README.txt |
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Notes: |
text/plain |