| name | ant-participant-identification |
| description | 识别行动者网络理论中的参与者，包括人类和非人类行动者，以及他们的特征、关系和网络位置。当需要识别人类和非人类行动者、确定其角色和特征时使用此技能。 |
| version | 1.0.0 |
| author | socienceAI.com |
| tags | ["actor-network-theory","ANT","participant-identification","human-actors","non-human-actors","socio-technical"] |
ANT Participant Identification Skill
Overview
ANT参与者识别技能专注于识别和分类行动者网络中的各类参与者（人类和非人类），分析他们的特征、角色和在网络中的位置。该技能帮助研究者全面识别网络中的行动者，并理解他们的基本属性和关系。
When to Use This Skill
Use this skill when the user requests:
- Identification of actors in a socio-technical network
- Classification of human and non-human actors
- Analysis of actor characteristics and roles
- Mapping of initial network configuration
- Recognition of agency in both human and non-human actors
- Determination of actor importance in a network
- Identification of central vs. peripheral actors
Quick Start
When a user requests participant identification:
- Identify all relevant human actors
- Recognize non-human actors (technologies, objects, concepts)
- Classify actors by type and role
- Map actor relationships and connections
- Assess agency of different actors
Core Functions (Progressive Disclosure)
Primary Functions
- Human Actor Recognition: Identify individuals, organizations, groups
- Non-Human Actor Recognition: Identify technologies, materials, concepts, documents
- Actor Classification: Categorize actors by type and function
- Agency Assessment: Evaluate the agency of different actors
Secondary Functions
- Role Analysis: Determine the roles actors play in the network
- Position Mapping: Map actors' positions within the network
- Relationship Identification: Identify connections between actors
- Influence Assessment: Evaluate actors' potential influence
Advanced Functions
- Actor Translation Tracking: Follow how actors' roles change
- Hybrid Actor Analysis: Analyze actors that combine human/non-human elements
- Actor Network Boundaries: Define network boundaries
- Actor Power Dynamics: Examine power relations between actors
Detailed Instructions
1. Human Actor Identification
- Identify individual actors (persons, experts, users, etc.)
- Identify organizational actors (companies, institutions, agencies)
- Identify collective actors (communities, movements, groups)
- Determine actors' interests and motivations
- Assess actors' resources and capabilities
2. Non-Human Actor Recognition
- Identify technological actors (devices, systems, platforms)
- Identify material actors (resources, infrastructure, tools)
- Identify conceptual actors (theories, ideas, frameworks)
- Identify document actors (policies, contracts, records)
- Identify natural actors (environment, climate, geography)
3. Actor Classification
- Classify by agency type (high, medium, low)
- Classify by network position (central, peripheral, bridging)
- Classify by role (mediator, intermediary, translator)
- Classify by stability (stable, changing, temporary)
4. Agency Assessment
- Evaluate the capacity to act and influence
- Assess the ability to mediate other actors' actions
- Determine the degree of autonomy in action
- Consider the actor's capacity to resist or modify influences
5. Relationship Mapping
- Identify direct connections between actors
- Map the strength of relationships
- Assess the nature of interactions (supportive, conflicting, neutral)
- Consider potential for translation between actors
6. Network Position Analysis
- Identify central vs. peripheral actors
- Determine brokerage or bridging positions
- Assess the actor's connectivity within the network
- Consider the actor's role in network stability
Parameters
actor_type: Type of actor to focus on (human, non-human, hybrid)network_scope: Scope of network to analyzeagency_level: Level of agency to assess (high, medium, low)role_focus: Specific roles to emphasize (mediator, intermediary, translator)classification_scheme: Framework for classifying actorsboundary_criteria: Criteria for defining network boundariesmethodology: Approach to actor identification (qualitative, ethnographic, etc.)
Examples
Example 1: Technology Adoption Network
User: "Identify actors in the adoption of electric vehicles in China"
Response: Identify government agencies, car manufacturers, consumers, charging infrastructure, battery technology, environmental concerns, regulations.
Example 2: Healthcare Network
User: "Identify actors in a telemedicine implementation"
Response: Identify doctors, patients, hospital administrators, medical devices, digital platforms, health data, medical protocols, regulatory bodies.
Example 3: Policy Implementation
User: "Identify actors in rural education policy implementation"
Response: Identify government officials, teachers, students, parents, schools, educational technology, textbooks, internet infrastructure, local communities.
Quality Standards
- Apply generalized symmetry principle (equal consideration of human/non-human actors)
- Identify both obvious and hidden actors
- Consider actors at multiple scales (local, regional, global)
- Assess agency rather than assuming it
- Maintain focus on relational properties of actors
Output Format
- Complete actor inventory with classifications
- Actor characteristic profiles
- Network position mappings
- Agency assessment matrices
- Relationship connection maps
Resources
- Actor-Network Theory literature (Latour, Callon, Law)
- Actor identification methodologies
- Science and Technology Studies resources
- Examples of actor identification in Chinese context
Metadata
- Compatibility: Claude 3.5 Sonnet and above
- Domain: Science and Technology Studies, Sociology
- Language: Optimized for Chinese research context
