aif version: AIF-Core-2011
mesh aif profile: 1.0
exported at: 2026-08-30T19:16:44.022Z
attestation:
  identifier: RrE6RhBB
  argument id: cmpux5yzf000c8c4430m6zbwx
  permalink: https://www.isonomia.app/a/RrE6RhBB
  version: 1
  immutable permalink: https://www.isonomia.app/a/RrE6RhBB@80e714a35b4d8a9dbe83417274284ed58f320cd95d83ea03807d6d332fd574b5
  iso id: iso:argument:RrE6RhBB
  iso url: https://www.isonomia.app/iso/argument/RrE6RhBB
  retrieved at: 2026-08-30T19:16:43.877Z
  created at: 2026-06-01T08:00:59.738Z
  updated at: 2026-06-01T08:00:59.738Z
  conclusion:
    claim id: cmpux5v4g00018c44ujfc117a
    text: Across more than 200 organizations in coordinated multi-country four-day-week trials (2022-2025), employee well-being outcomes - reduced burnout, stress, and fatigue, plus improved job and life satisfaction - improved sharply and consistently, and these gains largely held at 12-month follow-up.
  premises:
    claim id: cmpux5vr000038c44wc0kuxu7
    text: Well-being gains were stable at a 12-month follow-up rather than fading once the novelty wore off.
    is implicit: false
  premises:
    claim id: cmpux5wa000068c44vo12tcbv
    text: In the pooled six-country study of income-preserving four-day weeks (Fan, Schor et al., Nature Human Behaviour 2025), workers reported improved mental and physical health, higher job satisfaction, and reduced burnout, with gains partly driven by reduced fatigue and fewer sleep problems.
    is implicit: false
  premises:
    claim id: cmpux5wa000078c44xbpalmma
    text: The German national trial corroborated self-reported well-being gains with objective measures - wearable-tracked sleep (+38 min/week), heart-rate, and hair-cortisol analysis - reducing reliance on self-report alone for the well-being outcomes.
    is implicit: false
  scheme:
    id: cmoqrmr1e00068c7tl5hkvfj1
    key: statistical_generalization
    name: Argument from Sample to Population (Statistical Generalization)
    canonical key: statistical_generalization
    catalogue health:
      is argument pattern: true
      is dialogue meta: false
      is test placeholder: false
      canonical key: statistical_generalization
      cluster tag missing: false
      fingerprint materialised: true
  critical questions:
    scheme key: statistical_generalization
    total: 5
    unanswered:
      cq key: measurement_validity?
      text: Does the operational measure of F in the sample actually capture F as it is meant in the population-level claim?
      attack kind: UNDERMINES
      scheme key: statistical_generalization
      premise type: ORDINARY
      is scheme required: true
      inherited from parent scheme: false
      challenged: false
      challenge count: 0
      cq requires evidence: false
      cq burden: PROPONENT
      answer self canonical: true
      answer author kind: AI
      status: missing
    unanswered:
      cq key: representativeness?
      text: Is the sample actually representative of the target population on the dimensions that matter for F (demographics, behavior, time period, platform mix)?
      attack kind: UNDERMINES
      scheme key: statistical_generalization
      premise type: ORDINARY
      is scheme required: true
      inherited from parent scheme: false
      challenged: false
      challenge count: 0
      cq requires evidence: false
      cq burden: PROPONENT
      answer self canonical: true
      answer author kind: AI
      status: missing
    unanswered:
      cq key: sample_size?
      text: Is the sample large enough to support the precision (margin m) being claimed?
      attack kind: UNDERCUTS
      scheme key: statistical_generalization
      premise type: ORDINARY
      is scheme required: true
      inherited from parent scheme: false
      challenged: false
      challenge count: 0
      cq requires evidence: false
      cq burden: PROPONENT
      answer self canonical: true
      answer author kind: AI
      status: missing
    unanswered:
      cq key: scope_of_generalization?
      text: Does the conclusion stay within the population P from which S was drawn, or does it overreach (different country, different time period, different platform)?
      attack kind: UNDERCUTS
      scheme key: statistical_generalization
      premise type: ORDINARY
      is scheme required: true
      inherited from parent scheme: false
      challenged: false
      challenge count: 0
      cq requires evidence: false
      cq burden: PROPONENT
      answer self canonical: true
      answer author kind: AI
      status: missing
    unanswered:
      cq key: selection_effect?
      text: Was the sample drawn or recruited in a way that systematically biases the proportion of F (e.g., volunteer bias, opt-in panels, attrition)?
      attack kind: UNDERMINES
      scheme key: statistical_generalization
      premise type: ORDINARY
      is scheme required: true
      inherited from parent scheme: false
      challenged: false
      challenge count: 0
      cq requires evidence: false
      cq burden: PROPONENT
      answer self canonical: true
      answer author kind: AI
      status: missing
  dialectical status:
    incoming attacks: 0
    incoming supports: 0
    incoming attack edges: 0
    critical questions required: 5
    critical questions answered: 0
    critical questions open: 5
    standing score: 0.5
    is tested: false
    testedness: untested
    standing state: untested-default
    standing depth:
      challengers: 0
      independent reviewers: 0
      confidence: thin
    fitness breakdown:
      total: 0
      weights:
        cq answered: 1
        support edges: 0.5
        attack edges: -0.7
        attack c as: -1
        evidence with provenance: 0.25
  deliberation: My Arguments
    id: cmp6823ov00008cbr3jjscs6o
  author:
    id: mcp-bot
    display name: Isonomia MCP Bot
    kind: AI
    ai provenance:
      via: mcp
      tool: propose_structured_argument
      created at: 2026-06-01T08:00:59.736Z
round trip soundness:
  scheme key: statistical_generalization
  groups:
    ORDINARY:
      cq count: 5
      aif exposure: exposed
    ASSUMPTION:
      cq count: 0
      aif exposure: absent
    EXCEPTION:
      cq count: 0
      aif exposure: absent
  lossy: false

cmpux5v4g00018c44ujfc117a
type: InformationNode
text: Across more than 200 organizations in coordinated multi-country four-day-week trials (2022-2025), employee well-being outcomes - reduced burnout, stress, and fatigue, plus improved job and life satisfaction - improved sharply and consistently, and these gains largely held at 12-month follow-up.

cmpux5vr000038c44wc0kuxu7
type: InformationNode
text: Well-being gains were stable at a 12-month follow-up rather than fading once the novelty wore off.

cmpux5wa000068c44vo12tcbv
type: InformationNode
text: In the pooled six-country study of income-preserving four-day weeks (Fan, Schor et al., Nature Human Behaviour 2025), workers reported improved mental and physical health, higher job satisfaction, and reduced burnout, with gains partly driven by reduced fatigue and fewer sleep problems.

cmpux5wa000078c44xbpalmma
type: InformationNode
text: The German national trial corroborated self-reported well-being gains with objective measures - wearable-tracked sleep (+38 min/week), heart-rate, and hair-cortisol analysis - reducing reliance on self-report alone for the well-being outcomes.

cmpux5yzf000c8c4430m6zbwx
type: RA
type: statistical generalization
uses scheme: statistical_generalization
name: Argument from Sample to Population (Statistical Generalization)
applies scheme key: statistical_generalization

Premise
from: I:cmpux5vr000038c44wc0kuxu7
to: S:cmpux5yzf000c8c4430m6zbwx

Premise
from: I:cmpux5wa000068c44vo12tcbv
to: S:cmpux5yzf000c8c4430m6zbwx

Premise
from: I:cmpux5wa000078c44xbpalmma
to: S:cmpux5yzf000c8c4430m6zbwx

Conclusion
from: S:cmpux5yzf000c8c4430m6zbwx
to: I:cmpux5v4g00018c44ujfc117a

count: 0
weight: 1
contribution: 0

count: 0
weight: 0.5
contribution: 0

count: 0
weight: -0.7
contribution: 0

count: 0
weight: -1
contribution: 0

count: 0
weight: 0.25
contribution: 0