| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Studies how individuals and groups engage in political life: voting, participation, identity formation, mobilization, protest, activism, cooperation, conflict, political persuasion, social movements, and collective-action dynamics. Includes turnout, political psychology, mass opinion, identity politics, revolution and rebellion dynamics, and group coordination problems. Excludes institutional rules themselves (covered under political institutions) except as constraints on behavior; excludes macro outcomes unless emergent from collective action. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates at individual, group, and mass-public scales over short to long timescales: election cycles, protest waves, generational identity formation, organizational mobilization, and rapid-onset collective-action events. |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | Individuals; voters; activists; groups; organizations; identities; preferences; beliefs; attitudes; networks; communication channels; leaders/entrepreneurs; political messages; mobilization resources; grievances; collective goals. |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Preferences; attitudes; partisanship; ideology; identity salience; social embeddedness; group norms; trust and cooperation; coordination capacity; grievance intensity; perceived efficacy; network connectivity; emotional activation; susceptibility to persuasion. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Participation types (voting, protest, activism, volunteering); identities (ethnic, partisan, ideological, religious); mobilization forms (formal organizations, grassroots, digital activism); grievance-based vs opportunity-based collective action; conflict vs cooperation; persuasion vs polarization; mass movements vs small activist groups. |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | Turnout rates; protest size; identity strength; ideology measures; preference intensity; belief accuracy; mobilization resources; network ties; group size; coordination levels; grievance indicators; perceived risk; thresholds for participation; emotional states (anger, fear, enthusiasm). |
| | Parameterization | How variables encode and represent the system’s state. | Encoded via survey responses, turnout statistics, protest-event data, ideological scales, partisanship scores, network graphs, resource allocation metrics, mobilization models, threshold parameters, opportunity structures, and psychological indicators. |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Rational-choice participation assumptions; stable preferences; homogeneous group identities; uniform grievance levels; perfect communication in networks; no misinformation; costless participation; linear mobilization effects; clear leadership structure. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | Break down under misinformation, propaganda, emotional activation, heterogeneous identities, network fragmentation, fear/coercion, dynamic preference change, resource scarcity, repression, non-linear thresholds, conflict escalation, decentralized leaderless movements. |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | Individuals form preferences and identities that influence political behavior; groups coordinate through networks and leadership; costs/benefits shape participation; grievances and emotions affect mobilization; collective action faces free-rider and coordination barriers; communication and information diffusion shape opinion and action. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes measurable attitudes approximate internal beliefs; assumes behavioral responses emerge from observable contexts; assumes group identities are meaningful predictors; assumes communication networks exist; assumes collective-action incentives are structured and stable enough to model. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Behavioral predictions must align with psychological and sociological principles; mobilization models must be consistent with network structure; collective-action thresholds must match observed group behavior; survey-based attitudes must correspond to actual behavior patterns; grievance and opportunity models must not contradict participation outcomes. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Requires harmony among political psychology, social identity theory, rational-choice models, network theory, group-coordination frameworks, and conflict/persuasion models. Must integrate with institutional and macro-political environments without contradiction. |
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | Voting turnout; partisan vote share; protest size/frequency; petition signatures; activist participation; political donations; social media political engagement; mobilization waves; polarization indicators; violence levels; coordination failures; cascades and tipping-point events; diffusion of protest across regions or networks. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Hidden preferences (preference falsification); inability to observe private grievances; underreporting of protest participation; biased or censored authoritarian data; unobservable network ties; incomplete detection of online mobilization; difficulty observing early-stage collective-action formation; inability to directly measure emotional states or internal motivations. |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | Percent turnout; vote shares; protest counts; crowd-size estimates; participation rates; donation amounts; ideological scale scores; identity salience indices; network centrality metrics; grievance indicators; mobilization thresholds; sentiment scores. |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | Surveys (ANES, ESS, WVS); panel surveys; exit polls; protest-event databases; crowd-estimation tools; donation registries; voter files; social media scraping tools; sentiment-analysis systems; GPS/mobility data for protest mapping; network-analysis software. |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Turnout defined as share of eligible voters casting ballots; protest defined as public collective demonstration; identity strength defined via self-report scales; ideology defined via standardized issue-position scales; mobilization defined as transition from individual grievance to collective action; polarization defined as ideological/extremity distance; political engagement defined through measurable acts. |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | Survey sampling and weighting; coding protest events; recording turnout and vote totals; estimating crowd sizes; applying sentiment models; measuring network ties; conducting experiments (framing, persuasion, mobilization appeals); constructing longitudinal behavioral datasets; classifying political messages. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Regular election-data collection; systematic protest-event coding; standardized survey waves; longitudinal panel retention; cross-national harmonization; scraping publicly available digital-behavior data; auditing official participation statistics; mixed-method data triangulation. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Population sampling for surveys; stratified sampling across demographics; sampling activists vs non-activists; sampling protest events by region/time; sampling network nodes for relational data; sampling social-media content; sampling political organizations and mobilization campaigns. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | Survey microdata; turnout/vote totals; protest-event logs; social-media text datasets; identity and ideology scales; longitudinal behavior records; network adjacency matrices; time-series mobilization indicators; coded qualitative reports. |
| | Resolution | The granularity or precision with which data is captured. | Determined by survey frequency and sample size; protest-event reporting precision; granularity of geolocation data; depth of network mapping; temporal resolution of digital data; accuracy of crowd-estimation; noise in self-reported attitudes. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Cross-validating survey results with behavioral data; triangulating protest counts using multiple sources; calibrating sentiment models with hand-coded samples; validating identity/ideology scales; correcting turnout records using administrative audits; comparing mobilization metrics across datasets; adjusting network measures for sampling bias. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Sampling bias; nonresponse bias; measurement error in political attitudes; false or manipulated digital content; crowd-size estimation error; event underreporting; misclassification of protest type; inaccurate network inference; social-desirability bias; recall bias in self-reports. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Turnout rises with mobilization and perceived efficacy; group identity predicts stable voting patterns; polarization follows sorting and identity reinforcement; protest follows grievance × opportunity; contagion dynamics in social movements via networks; threshold models of participation; collective-action free-riding regularities; mass opinion shifts follow elite cues; preference falsification breaks suddenly under cascade dynamics. |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Stable partisan identification; ideological constraint; identity salience persistence; long-run turnout differentials across demographic groups; consistent influence of social networks; stable grievance structures; invariant protest-risk thresholds for specific regimes; persistence of collective-action problems (free-rider incentives). |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | Identity activation driving preference formation; social networks transmitting information/emotion; mobilization entrepreneurs lowering participation costs; grievance accumulation raising activation potential; coordination mechanisms enabling collective action; repression shaping participation risk; informational cascades triggering mass mobilization; persuasion mechanisms shifting attitudes; elite framing steering public opinion. |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Message → cognitive framing → attitude shift → behavioral response; Identity salience → group norm activation → political action; Grievance → mobilization cue → threshold crossing → cascade → mass movement; Elite cue → media diffusion → partisan alignment; Network exposure → contagion → coordinated protest; Repression → increased cost → dampened mobilization (or backlash). |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Identity, ideology, partisanship, turnout, mobilization, grievance, collective action, free rider, threshold model, social network, persuasion, framing, diffusion, polarization, cascade, public opinion, participation costs, risk perception, movement entrepreneurship, coordination. |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Participation types: voting, protest, activism, digital mobilization; Identities: ethnic, partisan, ideological, religious; Mobilization forms: grassroots, organizational, elite-driven, digital networks; Collective-action models: threshold, coordination games, cascade models; Behavioral modes: expressive, instrumental, emotional, identity-driven. |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | Threshold participation condition: participate if (u_i – c_i + k(\text{others}) ≥ 0); Opinion-update equations in bounded-confidence or Bayesian models; Network contagion models; Utility of participation vs abstention; Identity alignment functions; Protest diffusion equations; Persuasion models using signal-updating; Coordination-game payoff matrices. |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Threshold/cascade models (Granovetter); network diffusion models; spatial ideological models; turnout decision models; grievance–opportunity models; coordination-game simulations; mass-movement escalation models; framing/persuasion models; public-opinion dynamics; collective-risk dilemma models. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Homogeneous groups; rational-choice participation; perfect information; costless communication; static identities; linear mobilization functions; uniform grievance levels; centralized leadership; symmetric coordination incentives; absence of repression or misinformation. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Fail under misinformation or propaganda; emotional mobilization; fragmented networks; asymmetric repression; heterogeneous identities; multi-peaked ideological distributions; online–offline dynamic divergence; spontaneous leaderless movements; nonlinear radicalization; hidden or fluid identities. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Social-identity theory + political participation; network theory unifying contagion and coordination; rational-choice models unifying turnout, protest, and group action; grievance–opportunity–mobilization triad; bounded-rationality and political psychology frameworks; collective-action theory unifying cooperation problems across domains. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Psychology (emotion, cognition, identity); sociology (social movements, networks); economics (public goods, free-riding); communication studies (media effects, persuasion); anthropology (collective identity); complexity science (cascades, tipping points); criminology (riot dynamics). |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Randomizing political messages, frames, or cues; manipulating identity salience; varying mobilization appeals; running field experiments on turnout interventions (mailers, canvassing, texting); incentivizing participation in lab coordination games; altering network exposure; testing repression–mobilization dynamics in controlled simulations. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Using surveys, panel data, protest-event records, digital traces, and ethnographic observation to capture natural political behavior; monitoring spontaneous mobilization waves; observing diffusion of protests or messages across networks; studying organic identity formation and polarization trajectories. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Testing causal effects of mobilization messages; evaluating identity-based voting/polarization hypotheses; validating threshold/cascade models against observed protest data; testing persuasion effects (e.g., elite cues, framing); evaluating grievance–opportunity models; testing turnout determinants; testing network contagion predictions. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Repeating mobilization experiments across populations or election cycles; replicating persuasion experiments using new mediums; re-coding protest-event data; validating panel-survey findings with alternative surveys; replicating network contagion analyses using different platforms; reproducing grievance–mobilization models with revised data. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Estimating causal effects (RCTs, IVs, diff-in-diff); analyzing turnout/protest determinants; modeling attitude formation; estimating network influence using spatial or graph-based models; identifying thresholds for collective action; evaluating emotional or identity effects; measuring polarization dynamics; constructing predictive mobilization models. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Comparing rational-choice, identity-based, and psychological participation models; testing network contagion vs independent activation; comparing threshold vs coordination-game models; evaluating grievance vs opportunity-driven mobilization; comparing digital vs offline mobilization mechanisms; contrasting elite-cue vs bottom-up opinion-formation models. |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Identifying nonresponse bias; correcting self-report errors; detecting manipulated digital content; separating true mobilization from bots or coordinated campaigns; distinguishing identity effects from confounders; accounting for social-desirability bias; measuring overlap bias in network inference; dealing with noisy protest-size estimates. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Random assignment in experiments; stratified sampling; weighting survey samples; using IVs for endogenous mobilization; pre-registering hypotheses; blinding coders in event classification; using multiple data sources to validate mobilization events; avoiding partisan or ideological framing in study design. |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Auditing coding rules; reanalyzing raw survey data; testing robustness of network-based causal inference; evaluating replication failures; cross-checking event databases; comparing results derived from different mobilization measures; reexamining psychological scales for validity; critiquing assumptions in cascade or threshold models. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Updating models to incorporate misinformation, emotional activation, or digital mobilization; revising assumptions about rational participation; adding multi-layered network structures; modifying grievance frameworks; integrating new findings from behavioral psychology; revising theories of polarization or identity-driven mobilization. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Full disclosure of sampling strategies, survey instruments, coding schemes, experimental protocols, statistical models, digital-data collection methods, preprocessing decisions, and assumptions; documentation of uncertainty in estimates and identification strategies. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Protecting respondent anonymity; avoiding psychological harm in mobilization experiments; preventing manipulation of political views beyond minimal-risk thresholds; respecting digital-data privacy; avoiding partisan misuse of research findings; ensuring reproducibility and responsible communication of politically sensitive results. |