spatansemenescundfeelingcertaintyshiftiness
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Extraction report: spatansemenescundfeelingcertaintyshiftiness

Summary

  • Pipeline outputs used:
  • Numeric values: papers/spatansemenescundfeelingcertaintyshiftiness/out/tables/camelot_stream_p10_t1.csv (Table 5)
  • Vignette/method/test-stat wording: papers/spatansemenescundfeelingcertaintyshiftiness/out/fulltext.md
  • No direct PDF or rendered page images were consulted.
  • Effect sizes computed in papers/spatansemenescundfeelingcertaintyshiftiness/analysis/effect_sizes.qmd (rendered to papers/spatansemenescundfeelingcertaintyshiftiness/analysis/effect_sizes.html).
  • Output YAML: papers/spatansemenescundfeelingcertaintyshiftiness/spatansemenescundfeelingcertaintyshiftiness.yaml

Manual interventions / PDF use (required disclosure)

  • None.

Open questions / uncertainties

[ANALYST_INPUT: YEAR_PUBLICATION_VENUE]

The pipeline outputs contain conflicting/partial publication metadata: - TEI header includes DOI 10.4396/2024205 and a submission note: “Received 29 07 2024; accepted 05 12 2024.” - The extracted Markdown header line reads “Published on February 10, 2014” (likely an extraction artifact).

Please confirm: 1) What to code for paper.year (currently 2024) and paper.published (currently Yes), and 2) The correct venue/journal info (if any) to include in paper.citation.

  • Your answer:

[ANALYST_INPUT: EFFECT_GRANULARITY_CONFIRMATION]

This extraction encodes four independent stakes contrasts (low vs high stakes) split by: - Certainty grouping (Uncertain vs Certain), and - Error-possibility mention (low error vs high error).

The paper also reports a stakes comparison collapsed across error within Uncertain and within Certain subsamples (t-tests), which is not separately encoded here to avoid overlapping/duplicated samples.

Please confirm that keeping the 4 split effects (s1_e1–s1_e4) is the desired meta-analytic granularity for this project.

  • Your answer:

[ANALYST_INPUT: EVIDENCE_MODERATOR_CODING]

Evidence was coded as External (Tracy’s evidence comes from studying a political map). If you prefer coding this as First Person (memory/learning) or null, please indicate the intended rule for map/study-based evidence in this project.

  • Your answer: