Working Draft — LGIT framework unpublished. Shared for feedback only. Please do not cite or distribute without permission.

Australian Cohesion Monitor

LGIT Methodology

The theoretical framework and metrics used to detect fragility cycles in social cohesion data. Grounded in the LGIT theory papers (P1-P4).

What is LGIT?Core Metrics Data & Limitations

What is LGIT?

LGIT (Legitimacy Governance over Institutional Time) is a theoretical framework for understanding how democratic legitimacy erodes or recovers after institutional shocks.

Complex Systems

Hysteresis, path dependence, regime shifts

Institutional Analysis

Trust dynamics, governance legitimacy

Social Psychology

Identity salience, grievance formation

Theory Papers

Governance Without Democracy (GWD)

Baseline legitimacy concepts

Mapping Fragility Cycles (MFC)

Decay rates and asymmetry

Legitimacy Decay (LD)

Inverse relationship detection

Political Economy of Permanent Grievance (PEPG)

Long-term dynamics

Core Metrics

1. Legitimacy Decay Rate (LDR)

From Mapping Fragility Cycles

"The slope of trust decline relative to grievance authority persistence"

LDR measures the rate of legitimacy change, not just the level. We use simple linear regression on trust time series to calculate the slope.

LDR = slope of linear regression on (year, trust_value) pairs

Interpretation: LDR < -1 = declining, |LDR| < 1 = stable, LDR > 1 = improving

2. Category Salience Index (CSI)

From Mapping Fragility Cycles & Legitimacy Decay

"Composite measure of identity/grievance salience combining discrimination and division"

CSI captures how "activated" identity-based grievances are in the population. High CSI means people are more likely to interpret experiences through an identity lens.

CSI = (discrimination_rate + division_perception) / 2

Both components are percentages (0-100). CSI ranges from 0-100.

Component 1

Discrimination rate

% experiencing discrimination in last 12 months

Component 2

Division perception

% agreeing "Australia is more divided than before"

3. Inverse Relationship Detection

KEY SIGNATURE

From Legitimacy Decay

"Trust in institutions ↓ while identity salience ↑ = closed legitimacy loop"

The core diagnostic of LGIT is detecting when trust is declining at the same timethat identity salience is rising. This creates a feedback loop:

Low trust → People seek alternative sources of meaning/belonging → Identity categories become more salient → Grievances interpreted through identity lens → Trust in institutions declines further → Repeat

Inverse = (trust_trend == "declining") AND (csi_trend == "improving")

We also calculate Pearson correlation. Negative correlation confirms inverse relationship.

4. Fragility Cycle Classification

Derived from P2 (MFC)

Based on the combination of trust trend and CSI trend, we classify the overall system state into one of four cycle types:

Escalatory Cycle

Trust declining AND CSI rising. Self-reinforcing feedback loop active.

trust_slope < -1 AND csi_slope > 1

Frozen Cycle

Trust declining but CSI stable. Grievance persists without escalation.

trust_slope < -1 AND |csi_slope| < 1

Stable

No significant trend in either direction. System in equilibrium.

|trust_slope| < 1 AND csi_slope ≤ 1

Recovering

Trust improving. System returning to pre-shock equilibrium.

trust_slope > 1

5. Asymmetry Index

From Mapping Fragility Cycles

"Max - Min across subgroups per year"

Aggregate statistics can hide diverging experiences. The Asymmetry Index tracks the gap between the most-affected and least-affected groups over time.

Asymmetry(year) = max(subgroup_values) - min(subgroup_values)

• Slope > 0.5 pts/year = asymmetry GROWING (divergence)
• Slope < -0.5 pts/year = asymmetry SHRINKING (convergence)
• |Slope| < 0.5 = asymmetry STABLE

Data & Limitations

Data Source

All data comes from the Scanlon Foundation Research Institute'sannual "Mapping Social Cohesion" reports (2015-2024). Data has been extracted from published PDFs and verified against source pages.

Series Break: 2017-2018

In 2018, Scanlon transitioned from telephone surveys to the Life in Australia™ online panel. This creates a methodology break. Values from 2017 and earlier may not be directly comparable to 2018 onwards.

Verified Trust Values

Trust values for 2018-2024 have been corrected based on Table 18 (MSC-2022) and subsequent reports:

Year	Trust %	Source
2018	28%	Table 18, p53
2019	36%	Table 18, p53
2020	54%	Table 18, p53 (July)
2021	44%	Table 18, p53
2022	41%	Table 18, p53
2023	36%	Table 12, p50
2024	33%	p72

Limitations

Exploratory analysis

This is a pilot application of LGIT to survey data. Results should be interpreted as pattern detection, not definitive diagnosis.

Limited subgroup data

We only have discrimination broken down by birthplace. More granular subgroups (age, income, political orientation) would strengthen asymmetry analysis.

Correlation ≠ causation

We detect patterns, not mechanisms. Why trust and salience move together requires separate analysis.

Linear assumption

Linear regression may miss non-linear dynamics. Low R² values suggest more complex models may be needed.

Code Implementation

The metrics described above are implemented in lgit-metrics.ts

calculateLegitimacyDecayRate(data)    // Linear regression slope
calculateCSITimeSeries(disc, div)     // CSI composite over time
detectInverseRelationship(trust, csi) // Key LGIT signature
calculateAsymmetryTrend(subgroups)    // Divergence tracking
runLGITAnalysis(...)                  // Full analysis with findings

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