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Working Draft — LGIT framework unpublished. Shared for feedback only. Please do not cite or distribute without permission.
The Scanlon analysis demonstrates LGIT on one dataset. The framework can go much deeper with richer data—and applies to any domain where legitimacy and trust matter.
LGIT is a diagnostic framework for understanding how trust and legitimacy behave over time. It transforms raw survey data into actionable intelligence.
Linear regression on any time series to detect decay or recovery rates.
Asymmetry Index tracking whether subgroups are converging or diverging over time.
Automatically classify systems as escalatory, frozen, stable, or recovering.
Identify when trust falls while salience rises—the signature of fragility.
Compare pre-shock, shock, and post-shock periods to assess recovery patterns.
Generate human-readable findings with statistical backing (R², correlation, slopes).
The Scanlon pilot uses aggregate national data with limited subgroups. With richer data, LGIT can reveal much more.
Current Scanlon pilot capabilities
With richer survey breakdowns
What longitudinal studies enable
The current analysis scratches the surface. With additional data from Scanlon Foundation, we could answer much more specific questions about Australian social cohesion.
How does trust/belonging differ between Labor, Liberal, Greens, One Nation voters?
Would reveal whether polarisation is symmetric or asymmetric—critical for intervention design.
Gen Z vs Millennials vs Gen X vs Boomers on all indicators.
Detect intergenerational divergence—are young Australians on a different trajectory?
Trust and belonging by socioeconomic quintile.
Identify whether cohesion decline is class-stratified (extractive) or universal.
State, metro/regional, and electorate-level breakdowns.
Detect spatial heterogeneity—is the 'divided Australia' narrative accurate?
Follow same respondents over multiple years.
Distinguish individual trajectory shifts from compositional change. True decay detection.
Pulse surveys immediately after major events (elections, crises, policy changes).
Measure shock impact and recovery velocity with precision. Detect hysteresis.
Any domain where trust, legitimacy, or belonging matter can benefit from LGIT analysis. The framework is sector-agnostic.
Depth potential: Track trust by department, tenure, role level. Detect frozen cycles in specific teams.
Depth potential: Panel studies like HILDA enable individual trajectory tracking—the gold standard for LGIT.
Depth potential: Track trust by outlet, topic, political leaning. Identify escalatory cycles.
Depth potential: Analyse by faculty, student origin, program type. Detect stalled integration.
Depth potential: Track by condition, demographic, service type. Identify at-risk populations.
Depth potential: Compare decay rates across nations. Identify policy transfer opportunities.
Detect fragility before it becomes crisis. Identify which subgroups are diverging from the mainstream before polarisation locks in.
Know where to focus resources. A frozen cycle needs different treatment than an escalatory one. Asymmetry tells you who needs attention.
Measure whether initiatives are working. Did trust recover? Did asymmetry shrink? Did the cycle type improve? Empirical accountability.
Understand trajectory, not just position. A system at 40% trust and improving is healthier than one at 50% and declining.
Traditional surveys tell you what people think. LGIT tells you whether it will change—and in which direction.
LGIT outputs aren't just academic. They drive real decisions.
Target policies at frozen vs escalatory cycles. Prioritise groups with growing asymmetry.
Present legitimacy as a strategic asset with trend data. ESG beyond compliance.
Measure whether cohesion initiatives actually shift trajectories, not just snapshots.
Generate publishable findings on temporal dynamics of trust and identity.
Whether you have existing survey data or need to design new measurement, the LGIT framework can help you understand legitimacy dynamics in your domain.