A forecast with competing outcomes
Recover the central forecast, alternatives, assumption and remote case.

Position competing outcomes on an evidence scale, justify nearby modal choices and revise a forecast when new evidence arrives.
For learners: Distinguish nearby probability choices, justify them from evidence, revise selectively and deliver a measured forecast.
For teachers: This C1 step turns calibrated claims into an evidence scale with neighbouring-choice justification, assumptions and revision triggers.
สำหรับผู้เรียน: แยกความต่างของตัวเลือกความน่าจะเป็นที่ใกล้กัน อธิบายจากหลักฐาน ปรับเฉพาะข้อสรุปที่ได้รับผล และนำเสนอการคาดการณ์อย่างพอดี
สำหรับครู: ขั้น C1 นี้พัฒนาข้อสรุปที่พอดีให้เป็นสเกลหลักฐาน พร้อมเหตุผลระหว่างตัวเลือกที่ใกล้กัน สมมติฐาน และจุดกระตุ้นให้ปรับข้อสรุป
Place ten probability statements on an evidence scale, justify six modal choices, revise three conclusions after new evidence, and deliver a measured forecast.
จัดวางข้อความความเป็นไปได้ 10 ข้อตามน้ำหนักหลักฐาน อธิบายเหตุผลของการเลือก modal 6 ข้อ ปรับข้อสรุป 3 ข้อเมื่อมีหลักฐานใหม่ และนำเสนอการคาดการณ์ที่พอดีกับข้อมูล
At C1, accuracy includes the distance between neighbouring probability choices. A useful forecast shows its centre, alternatives, assumptions and the evidence that would cause revision.
ภาษาไทย: ในระดับ C1 ความแม่นยำรวมถึงความแตกต่างระหว่างตัวเลือกความน่าจะเป็นที่อยู่ใกล้กัน การคาดการณ์ที่ดีต้องมีข้อสรุปหลัก ทางเลือก สมมติฐาน และหลักฐานที่จะทำให้ปรับข้อสรุป
Ask “Why this wording rather than the neighbouring option?” and “Which new evidence would move it?” C1 evidence is the justification and selective revision, not a long list of modal labels.
| Stage | Teacher move | Evidence to collect |
|---|---|---|
| 1 · Scale | Place ten claims by evidence strength. | Ten placements |
| 2 · Distinguish | Compare neighbouring probability choices. | Six justifications |
| 3 · Revise | Add three evidence changes and require selective revision. | Three revisions |
| 4 · Forecast | Build centre, alternatives, assumptions and triggers. | One forecast plan |
| 5 · Transfer | Use British/American recordings to recover and challenge choices. | Two listening maps |
| 6 · Perform | Score MEAN/FORM/USE, set one target and retry. | Forecast + focused retry |
Judge the relationship between claim and evidence, not merely how confident the speaker sounds.
| Evidence position | Typical language | Decision test |
|---|---|---|
| Strongly supported | must / is highly likely to | Do several independent clues converge? |
| More likely than not | is likely to / will probably | Does the evidence favour this over alternatives? |
| Open possibility | may / might / could | Does it fit without being preferred? |
| Weak possibility | could conceivably / may possibly | Is it plausible but poorly supported? |
| Ruled out | cannot / is highly unlikely to | Does reliable evidence contradict it? |
Do not teach the scale as fixed percentages. Require a comparison with the neighbouring position and a reason tied to evidence quality.
Explain why one wording fits better than a nearby alternative.
| Choice pair | Fine distinction | Evidence question |
|---|---|---|
| must vs highly likely to | strong inference vs high forecast probability | Are you explaining a present clue or forecasting an outcome? |
| likely to vs may well | preferred outcome vs notable possibility | Does one outcome clearly lead? |
| may vs might | often interchangeable; context and stance matter more than a fixed gap | What alternative remains open? |
| could vs could conceivably | ordinary possibility vs deliberately remote possibility | How weak is the support? |
| unlikely to vs cannot | low probability vs contradiction | Is the claim merely doubtful or actually ruled out? |
Modal probability is calibrated through context, evidence source and communicative stance. Avoid treating each form as one universal percentage.
Ask “Why this one rather than its neighbour?” A label without comparison does not demonstrate C1 control.
Change only the claims whose evidence position has moved.
| Initial conclusion | New evidence | Required revision |
|---|---|---|
| Demand is likely to rise. | A larger survey shows stable demand. | downgrade and explain the sample change |
| The delay may be temporary. | Three independent systems report the same fault. | strengthen the diagnosis |
| The plan cannot meet the deadline. | A verified extra team becomes available. | remove the impossible claim |
Reward selective revision. New evidence should not cause every claim to move; the learner must identify which inference it actually affects.
Turn current indicators into a forecast with range, assumptions and revision triggers.
| Forecast element | Required output | Quality check |
|---|---|---|
| Central forecast | one likely outcome | supported by at least two indicators |
| Alternative | two open possibilities | plausible and distinct |
| Remote case | one weak possibility | not impossible, but evidence-light |
| Boundary | one ruled-out claim | contradiction named |
Keep alternatives meaningfully different. A measured forecast shows a centre, a range and the evidence that would cause movement.
Recover probability positions across two accents and test them against new evidence.
Recover the central forecast, alternatives, assumption and remote case.
Track three revised conclusions and the evidence that moved each one.
Audio never starts automatically. Accent variety tests transfer, not imitation. Replay the evidence or assumption clause when the learner hears the modal but cannot justify it.
Deliver one independent measured forecast, revise it and improve one precise weakness.
Result: 0–2 Rebuild · 3–4 Developing · 5–6 C1 mastery evidence.
Score the first independent attempt across MEAN, FORM and USE. Give one target only: scale position, neighbouring distinction, evidence justification or selective revision. Keep the original score and record the retry.
You can now distinguish nearby modal-probability choices, justify them against evidence and revise a measured forecast selectively. Use the Dynamic Learning Graph below for the next step.
Return to the Learning LibraryLearn first: calibrate claims, limitations and conditional recommendations.
เรียนก่อน: ฝึกกำหนดน้ำหนักข้อสรุป ข้อจำกัด และคำแนะนำแบบมีเงื่อนไข
Compare the C1 probability scale with evidence-led present deduction.
เปรียบเทียบสเกลความน่าจะเป็นระดับ C1 กับการอนุมานปัจจุบันจากหลักฐาน
Use measured probability to revise scenario plans when conditions change.
นำระดับความน่าจะเป็นที่พอดีไปใช้ปรับแผนสถานการณ์เมื่อเงื่อนไขเปลี่ยน
Separate evidence strength from interpersonal distance in advanced modal stance.
แยกน้ำหนักหลักฐานออกจากระยะห่างระหว่างบุคคลในการใช้ modal ระดับสูง