schema_version: '1.2'
paper:
  paper_id: turrietalndactionabilityjudgmentscause
  citation: 'Turri, J., Buckwalter, W., & Rose, D. (2016). Actionability Judgments Cause Knowledge Judgments. Thought: A Journal
    of Philosophy, 5(3), 212–222.'
  short_label: Turri et al. 2016
  doi: 10.1002/tht3.213
  published: 'Yes'
  year: 2016
  language: English
  language_other: null
  research_objective: Test whether actionability judgments cause knowledge judgments (or vice versa) using causal modeling
    across two experiments manipulating practical stakes.
  data_availability:
    data_available_online: null
    url: null
    notes: null
  notes: null
studies:
- study_id: 1
  label: Experiment 1
  language: English
  language_other: null
  objective: Test the relationship between stakes, actionability judgments, and knowledge judgments in an intelligence-analyst
    vignette (Low vs High stakes; multiple dependent measures including knowledge).
  sample:
    n_final: 200
    recruitment: mTurk
    recruitment_other: null
    compensation: money
    compensation_other: $0.40 for approximately 2–3 minutes.
    characteristics: Aged 18–68 years (mean age = 31); 94% reported English as a native language; 80 females.
    mean_age: 31.0
    mean_age_prov:
      page: null
      quote: Two hundred participants (aged 18-68 years, mean age = 31 years; 94% reporting English as a native language;
        80 females) were tested.
    provenance:
      page: null
      quote: Two hundred participants (aged 18-68 years, mean age = 31 years; 94% reporting English as a native language;
        80 females) were tested. Participants were recruited and tested online using Amazon Mechanical Turk and Qualtrics
        and compensated $0.40 for approximately 2-3 minutes of their time.
  design: Between-Subjects
  design_other: null
  manipulated_factors: []
  paradigm: Agreement with knowledge claim
  paradigm_other: null
  scale:
    label: Likert 7-point
    points: 7
    anchors: '"strongly disagree" … "strongly agree"'
    direction: Higher numbers indicate stronger agreement with the statement.
    provenance:
      page: null
      quote: Responses were collected on a standard 7-point Likert scale anchored with "strongly disagree," "disagree," "somewhat
        disagree," "neutral," "somewhat agree," "agree," and "strongly agree," left-to-right on the participant's screen.
        Responses were coded 1 (strongly disagree) to 7 (strongly agree).
  measures:
    knowledge_question_text: Jennifer knows that Ivan no longer [jogs regularly/is a threat].
    knowledge_question_first: null
    additional_question_text: null
  effects:
  - effect_id: s1_e1
    subgroup: Experiment 1 — Knowledge judgment
    subgroup_desc: Agreement with third-person knowledge attribution (Jennifer knows …)
    design: Between-Subjects
    design_other: null
    moderators:
      scenario: other
      skeptical_pressure: 'No'
      awareness: 'Yes'
      evidence: External
      attribution_person: Other
      evidence_reliability: Medium
    moderators_coding:
      scenario:
        provenance:
          page: null
          quote: Jennifer is an intelligence analyst developing a file on Ivan, an elusive foreign operative.
          tei_id: null
          table_ref: null
        reason: The vignette is about an intelligence report on a foreign operative (not bank/peanuts/bridge/typos).
      skeptical_pressure:
        provenance:
          page: null
          quote: Jennifer has a source who tells her that Ivan stopped [his low-carb diet/selling arms to terrorists] and
            is no longer [jogging regularly/a threat].
          tei_id: null
          table_ref: null
        reason: No explicit doubt/counterconsideration is introduced; the protagonist receives testimony from a source.
      awareness:
        provenance:
          page: null
          quote: If Ivan still [jogs regularly/is a threat], there will be serious consequences.
          tei_id: null
          table_ref: null
        reason: The stakes (serious consequences) are explicitly described in the scenario presented to participants.
      evidence:
        provenance:
          page: null
          quote: Jennifer has a source who tells her that Ivan stopped [his low-carb diet/selling arms to terrorists] and
            is no longer [jogging regularly/a threat].
          tei_id: null
          table_ref: null
        reason: The evidence basis is testimony from another person (a source), i.e., external evidence.
      attribution_person:
        provenance:
          page: null
          quote: Jennifer knows that Ivan no longer [jogs regularly/is a threat].
          tei_id: null
          table_ref: null
        reason: Participants rate agreement with a third-person attribution ('Jennifer knows …'), not a self-ascription.
      evidence_reliability:
        provenance:
          page: null
          quote: Jennifer has a source who tells her that Ivan stopped [his low-carb diet/selling arms to terrorists] and
            is no longer [jogging regularly/a threat].
          tei_id: null
          table_ref: null
        reason: The source’s reliability is not specified/manipulated, so evidence_reliability is coded as null.
    contrast:
      group_high: high
      group_low: low
      sign_convention: d = mean(low) - mean(high)
      other_notes: 7-point agreement rating; higher = more agreement with knowledge statement.
    groups:
    - group_id: low
      label: null
      n: null
      mean: 4.53
      sd: 1.73
      se: null
      provenance:
        page: 5
        quote: Knowledge,4.53 (1.73),3.49 (1.65),4.36,198.0,<.001,1.04,0.62,0.57 1.51
        tei_id: null
        table_ref: tabula_stream_p5_t1.csv
    - group_id: high
      label: null
      n: null
      mean: 3.49
      sd: 1.65
      se: null
      provenance:
        page: 5
        quote: Knowledge,4.53 (1.73),3.49 (1.65),4.36,198.0,<.001,1.04,0.62,0.57 1.51
        tei_id: null
        table_ref: tabula_stream_p5_t1.csv
    reported_test:
      test: t
      t: 4.36
      df1: 198.0
      reported_d: 0.62
      notes: Independent samples t-test; table reports d.
      provenance:
        page: 5
        quote: Knowledge,4.53 (1.73),3.49 (1.65),4.36,198.0,<.001,1.04,0.62,0.57 1.51
        table_ref: tabula_stream_p5_t1.csv
    effect_size:
      metric: SMD
      d: 0.62
      v: 0.021192068909
      computed_from: reported_d
      needs_review: false
      notes: d from Table 1; v computed from reported d + t(df) in analysis/effect_sizes.qmd (method=between_reported_d_t_df).
    quality_flags: []
    notes: null
  notes: null
- study_id: 2
  label: Experiment 2
  language: English
  language_other: null
  objective: Replicate the causal-modeling approach in a coffee-menu vignette with a Low vs High stakes manipulation; dependent
    measures include actionability and knowledge judgments.
  sample:
    n_final: 205
    recruitment: mTurk
    recruitment_other: null
    compensation: money
    compensation_other: null
    characteristics: Aged 18–72 years (mean age = 32); 96% reported English as a native language; 101 females.
    mean_age: 32.0
    mean_age_prov:
      page: null
      quote: Two hundred and five participants (aged 18-72 years, mean age = 32 years; 96% reporting English as a native language;
        101 females) were tested.
    provenance:
      page: null
      quote: Two hundred and five participants (aged 18-72 years, mean age = 32 years; 96% reporting English as a native language;
        101 females) were tested.
  design: Between-Subjects
  design_other: null
  manipulated_factors: []
  paradigm: Agreement with knowledge claim
  paradigm_other: null
  scale:
    label: Likert 7-point
    points: 7
    anchors: '"strongly disagree" … "strongly agree"'
    direction: Higher numbers indicate stronger agreement with the statement.
    provenance:
      page: null
      quote: Responses were collected on a standard 7-point Likert scale anchored with "strongly disagree," "disagree," "somewhat
        disagree," "neutral," "somewhat agree," "agree," and "strongly agree," left-to-right on the participant's screen.
        Responses were coded 1 (strongly disagree) to 7 (strongly agree).
  measures:
    knowledge_question_text: Christina knows that the coffee [is from northern Colombia/contains pine nuts].
    knowledge_question_first: null
    additional_question_text: null
  effects:
  - effect_id: s2_e1
    subgroup: Experiment 2 — Knowledge judgment
    subgroup_desc: Agreement with third-person knowledge attribution (Christina knows …)
    design: Between-Subjects
    design_other: null
    moderators:
      scenario: peanuts
      skeptical_pressure: 'No'
      awareness: 'Yes'
      evidence: First Person
      attribution_person: Other
      evidence_reliability: Medium
    moderators_coding:
      scenario:
        provenance:
          page: null
          quote: Christina knows that the coffee [is from northern Colombia/contains pine nuts].
          tei_id: null
          table_ref: null
        reason: The vignette concerns whether food contains nuts (pine nuts), matching the peanuts/allergy scenario family.
      skeptical_pressure:
        provenance:
          page: null
          quote: Christina observes that the latest shipment of coffee [is from northern Colombia/contains trace amounts of
            pine nuts].
          tei_id: tab_2
          table_ref: null
        reason: No explicit counterconsideration or doubt is introduced; the protagonist observes the relevant fact.
      awareness:
        provenance:
          page: null
          quote: If the coffee [is from northern Colombia/contains pine nuts], there could be serious consequences.
          tei_id: null
          table_ref: null
        reason: The stakes (serious consequences) are explicitly described in the scenario presented to participants.
      evidence:
        provenance:
          page: null
          quote: Christina observes that the latest shipment of coffee [is from northern Colombia/contains trace amounts of
            pine nuts].
          tei_id: tab_2
          table_ref: null
        reason: The evidence is based on the protagonist’s own observation (first-person evidence).
      attribution_person:
        provenance:
          page: null
          quote: Christina knows that the coffee [is from northern Colombia/contains pine nuts].
          tei_id: null
          table_ref: null
        reason: Participants rate agreement with a third-person attribution ('Christina knows …'), not a self-ascription.
      evidence_reliability:
        provenance:
          page: null
          quote: Christina observes that the latest shipment of coffee [is from northern Colombia/contains trace amounts of
            pine nuts].
          tei_id: tab_2
          table_ref: null
        reason: The paper does not specify/manipulate source reliability, so evidence_reliability is coded as null.
    contrast:
      group_high: high
      group_low: low
      sign_convention: d = mean(low) - mean(high)
      other_notes: 7-point agreement rating; higher = more agreement with knowledge statement.
    groups:
    - group_id: low
      label: null
      n: null
      mean: 6.13
      sd: 1.11
      se: null
      provenance:
        page: 7
        quote: Knowledge,6.13 (1.11),6.19 (1.13),−0.38,203.0,.702,−0.06,0.05,−0.37 0.25
        tei_id: null
        table_ref: tabula_stream_p7_t2.csv
    - group_id: high
      label: null
      n: null
      mean: 6.19
      sd: 1.13
      se: null
      provenance:
        page: 7
        quote: Knowledge,6.13 (1.11),6.19 (1.13),−0.38,203.0,.702,−0.06,0.05,−0.37 0.25
        tei_id: null
        table_ref: tabula_stream_p7_t2.csv
    reported_test:
      test: t
      t: -0.38
      df1: 203.0
      p: 0.702
      reported_d: 0.05
      notes: Independent samples t-test; table reports |d|, so sign is inferred from group means (mean_low < mean_high).
      provenance:
        page: 7
        quote: Knowledge,6.13 (1.11),6.19 (1.13),−0.38,203.0,.702,−0.06,0.05,−0.37 0.25
        table_ref: tabula_stream_p7_t2.csv
    effect_size:
      metric: SMD
      d: -0.05
      v: 0.017319177026
      computed_from: reported_d
      needs_review: false
      notes: d from Table 2 (signed using mean_low-mean_high); v computed from reported d + t(df) in analysis/effect_sizes.qmd
        (method=between_reported_d_t_df).
    quality_flags: []
    notes: null
  notes: null