rubenarslan / rubenarslan/rubenarslan.github.io

June 2019 reanalysis: codebook metadata is assigned to bfi instead of methods_time

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Description

June 2019 reanalysis: codebook metadata is assigned to bfi instead of methods_time

In the Codebook section of the June 14, 2019 reanalysis, the dataset name is assigned to metadata(methods_time), but the next six fields are assigned to metadata(bfi). The final call renders compact_codebook(methods_time).

Consequently the target dataset receives only its name. Its intended identifier, publication date, creator, citation, URL and temporal coverage are assigned to the unrelated example dataset. This is visible in the currently published JSON-LD: it lacks the DOI, creator, citation and source URL written in the Rmd, and shows a generated publication date instead.

Source at 24fe274.

I executed the original metadata assignment block on synthetic methods_time and bfi data frames, using the four actual metadata getter/setter S3 functions from codebook at 6433edb, in WebR 0.6.0 / R 4.6.0. Results:

  • Original block: target metadata contains only name; the DOI is written to bfi.
  • Replacing the six metadata(bfi) targets with metadata(methods_time): all seven fields are attached to the intended data frame, and bfi remains unchanged.

This is a focused metadata test, not a full article render or a reanalysis of the empirical results. I am not claiming any statistical conclusion changes. The package includes a bfi example dataset, so this report also does not assume that the original block must fail with an undefined-object error.

The object-name replacement is the minimal fix. Before republishing, datePublished = "2016-06-01" and temporalCoverage = "2016" should also be checked against the source archive, which lists first publication on February 22, 2019. I have not silently changed those fields.

I prepared this report and test with OpenAI Codex for GitHub account yunancun. If this qualifies under your scientific bounty's reproducibility category at the blog rate, I would like to claim €2.50, payable privately by PayPal after your assessment. I recognize that eligibility and classification are yours to determine, and am not treating this as an approved award. No payment details are included here.

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