Extract supporting and opposing evidence

작성자: AILesson7 분 소요테스트::ChatGPT검토일: 2026-08-28

빠른 답변

Build a balanced evidence table without forcing neutral or irrelevant material into two sides. 제공할 내용: Claim or decision question, Source material, Evidence rules. 예상 결과: A traceable pro, con, mixed, and unresolved evidence map with strength assessment.

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Claim or decision questionSource materialEvidence rules

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Extract evidence supporting and opposing the exact proposition.

Proposition, alternatives, definitions, scope, and use:
[question]

Sources with IDs, dates, authors, methods, and passages:
[sources]

Eligibility, population/time fit, independence, quality, and inference rules:
[rules]

Use only supplied material. Extract the smallest evidence unit that bears on the proposition and preserve context, measure, denominator, period, and limitations. Classify each as supports, opposes, mixed/conditional, neutral/context, or irrelevant; do not force every source into two sides. Separate a source author's conclusion from underlying data and your interpretation. For each item provide source locator, evidence statement, direction, directness, quality, independence, applicability, and what claim strength it permits. Identify shared underlying data, conflicts, publication bias risk, missing counterevidence, and conditions that reverse the conclusion. Finish with a balanced synthesis that weights evidence quality rather than counting items.

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입력에서 결과까지

적용 예시

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실제 입력

Claim or decision question: For a 120-person UK office, does replacing assigned desks with a 0.7 desk-per-person shared model improve space efficiency without materially reducing employees' ability to find a suitable workspace during peak days? 'Suitable' requires a desk in the needed quiet/collaboration zone. Use for pilot design, not a permanent decision.
Source material: S1 occupancy count, our office, six Tue–Thu weeks: peak simultaneous attendance 72–98; zones not recorded. S2 employee survey, 86/120 responses: 58 expect shared desks to be acceptable, 19 unsure, 9 oppose; hypothetical question. S3 facilities simulation: at 0.7 ratio (84 desks), average modeled utilization rises 51%→69%; assumes attendance independent by person and no zone constraint. S4 pilot at another 300-person firm: 0.65 ratio; 12 of 20 observed peak days had no free quiet-zone desks; total desks were still available; different hybrid policy. S5 vendor blog cites S4 and says sharing 'eliminates wasted space.'
Evidence rules: Direct internal observations high for our attendance but incomplete without zones. Simulations are conditional on assumptions. Surveys measure expectations, not actual workspace access. External firm evidence is indirect but relevant to zone risk. Vendor blog is not independent of S4. Preserve counts and do not define 'materially' without a threshold.

예시 출력

Supports efficiency: S3 models utilization rising 51%→69% with 84 desks; directness medium, conditional quality because independence and no-zone assumptions are untested. S1 shows many observed peaks below 84, but 72–98 means some peak counts exceed capacity; mixed rather than support. S2: 58/86 respondents expect acceptance; low directness for actual access and 34 non-respondents.

Opposes the combined proposition: S4 found no quiet-zone desk on 12/20 peak days despite free desks elsewhere; indirect applicability because ratio, scale, and hybrid policy differ, but directly demonstrates the zone-allocation mechanism. S1 also opposes an unconditional 84-desk model on days above 84 unless attendance is managed; it cannot assess zone suitability. S5 adds no independent evidence and its “eliminates” conclusion exceeds S4.

Missing: our peak distribution by zone and hour, team co-attendance, suitable-zone demand, day-level overflow frequency, and an agreed materiality threshold. Balanced synthesis: evidence conditionally supports higher modeled utilization, but does not establish acceptable peak access. The strongest internal observation already shows total-demand risk on some days, while external evidence makes zone mismatch plausible. Design a pilot with booking/overflow, zone-level counts, and a pre-agreed failure threshold rather than claim the combined proposition is supported.

효과가 있는 이유

1
A mixed and neutral category prevents artificial symmetry and loss of conditional findings.
2
Weighting directness, quality, independence, and applicability is more meaningful than citation counts.

결과 확인

Does each evidence unit retain its denominator, period, context, and limitations?
Are author conclusions, underlying observations, and analyst interpretations distinct?
Does the synthesis weight independence and applicability instead of counting sources?

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자주 묻는 질문

이 레시피를 언제 사용해야 하는지, 무엇을 제공해야 하는지, 그리고 어떤 부분에서 사람의 검토가 여전히 중요한지에 대한 실용적인 답변

What should I prepare before using “Extract supporting and opposing evidence”?

For “Extract supporting and opposing evidence,” prepare Claim or decision question, Source material, and Evidence rules. Replace placeholders only with information you can verify. If a detail is unknown, preserve that uncertainty explicitly instead of asking the model to infer it.

When is the “Extract supporting and opposing evidence” result not ready to use?

The result is not ready if it does not yet deliver the stated outcome—A traceable pro, con, mixed, and unresolved evidence map with strength assessment—from the supplied evidence, or if it relies on unresolved assumptions, missing approvals, or invented details. Use the checks as release gates: revise the source inputs or assign a named, authorized reviewer instead of polishing an unsupported output.

Which AI tools have recorded tests for “Extract supporting and opposing evidence”?

The published test record for “Extract supporting and opposing evidence” lists ChatGPT as of 2026-08-28. This confirms recorded runs, not guaranteed compatibility or identical results in later product versions. For another tool or version, keep every constraint visible and repeat the result checks before use.

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