Observable Evidence

Canonical definition

Claim status: CSI distinguishes observed evidence, extracted signals, and inferred collective states. Computational social data are discussed through Lazer et al. (2009). The CSI-specific requirements remain proposals.

The role of evidence in CSI

CSI reasons from observable traces to a latent collective-level construct. Recorded evidence must be distinguished from its interpretation; the meaning of an observation depends on construct, boundary, time, and context.

The same trace can support different interpretations. Reduced message volume might indicate disengagement, efficient coordination, task completion, or migration to another channel. CSI therefore requires evidence to be interpreted as part of a justified inferential chain rather than treated as a self-explanatory signal.

Requirements for defensible evidence

Major evidence categories

Computational social science demonstrates the analytic value of large-scale behavioral traces, while network scholarship shows that relational structure can carry information not reducible to isolated participant attributes (Lazer et al., 2009; Moody & White, 2003).

Evidence, feature, indicator, and criterion

Observation, signal extraction, and state modeling serve different roles. Evidence used to evaluate an estimate must also be distinguished from the observations and features used to produce it. An observed or derived feature does not by itself establish the target collective state.

Keeping these roles distinct reduces circular validation. A model should not claim success merely because it reproduces the same signal used to define its target.

Triangulation and multimodal evidence

CSI proposes triangulation across group-referenced surveys, trained observers, and independently coded interaction episodes. Triangulation does not eliminate disagreement about latent collective conditions.

More data are not automatically better. Multimodal systems increase risks of misalignment, surveillance, missingness, and unequal representation. Each source should add distinct construct-relevant value.

Evidence quality and missingness

• Coverage: determine whose activity and which channels are represented or absent.
• Reliability: assess whether the trace is consistently recorded and measured.
• Validity: test whether the evidence represents the intended theoretical meaning.
• Comparability: identify changes in tools, norms, access, or logging that affect interpretation.
• Missingness: model absence explicitly when missing data may be systematic or meaningful.

Illustrative example

A decline in meeting participation could be treated as evidence of lower engagement. Yet the same period may show faster workflow completion, stable response reciprocity, and a planned shift to asynchronous work. The participation decline alone is ambiguous.

A defensible CSI analysis would examine multiple evidence categories, apply the chosen composition model, account for the contextual change, and preserve uncertainty before estimating engagement.

Ethical and governance requirements

Observable evidence about human interaction can be sensitive even when it is technically accessible. As a normative CSI requirement, the program proposes using the least intrusive evidence sufficient for the research or decision purpose, minimizing identifiable data, documenting consent or lawful authority, securing the data, and limiting downstream use.

CSI requires calibrated uncertainty, explanation, contestability, and appropriate human oversight when estimates support human judgment. Users should be able to inspect evidence, challenge interpretations, supply context, and decline an intervention.

Common evidence errors

• Treating an easily measured proxy as though it were the construct itself
• Using sentiment, volume, or a network metric without construct-specific justification
• Combining evidence from different levels or time windows without explicit alignment
• Ignoring participants or channels that are systematically missing
• Using the same signal as both model input and supposedly independent validation criterion
• Collecting intrusive data whose incremental value has not been demonstrated

Relationship to the CSI construct system

The collective establishes the unit, the collective state defines the latent target, emergence explains the higher-level condition, and the composition model specifies how observable evidence relates to that target. CSI integrates the evidence, estimates the state and uncertainty, and evaluates the result through independent collective-level validation.

Selected scholarly foundations

The computational social-data foundation is Lazer et al. (2009); relational structure is discussed through Moody and White (2003). The CSI-specific evidence and validation requirements remain research-program proposals.

Research status

RA-006 documents the current CSI research-program position. It remains a foundational draft subject to scholarly review, empirical operationalization, and validation. The proposed capabilities and expected benefits have not been empirically established.