threshold-calibration

Research

Systematically sweep consensus thresholds to observe which items achieve consensus at what level, producing a threshold-consensus curve.

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Threshold Calibration

Systematically vary the consensus threshold to understand the sensitivity of consensus classification. Rather than picking a single arbitrary threshold, sweep across a range to see which items are robust consensus (agree at any threshold) vs. fragile (only consensus at lenient thresholds).

Stages

Sweep — Run threshold-sweep to compute consensus status at multiple threshold levels
Classify — Run consensus-classification to categorize items at the chosen operating threshold
Measure — Run consensus-measurement to validate final consensus scores

Available SOPs

SOP	Role in Tactic
threshold-sweep	Compute consensus at multiple threshold levels, produce curve
consensus-classification	Classify items as consensus/dissensus at operating threshold
consensus-measurement	Validate final consensus scores with appropriate method

Execution Guidance

Sweep range should cover 50%–90% agreement (or IQR 0.5–2.0)
Identify "knee" in the curve where many items flip classification
Robust consensus items (agree at strict thresholds) are highest confidence
Fragile items (only consensus at lenient thresholds) need flagging
Report both the curve and the classification at the chosen operating point

Minimum Yield

Threshold-consensus curve (threshold vs. number-of-consensus-items curve)
Classification results (classification at operating threshold: consensus items, dissensus items)

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
consensus-classification	Classify items as consensus or dissensus at a given threshold.
consensus-measurement	Compute consensus score from collected judgments using the appropriate statistical method.
threshold-sweep	Compute consensus status at multiple threshold levels to produce a threshold-consensus curve.