ant-participant-identification

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:

1. Identify all relevant human actors
2. Recognize non-human actors (technologies, objects, concepts)
3. Classify actors by type and role
4. Map actor relationships and connections
5. 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 analyze
• agency_level: Level of agency to assess (high, medium, low)
• role_focus: Specific roles to emphasize (mediator, intermediary, translator)
• classification_scheme: Framework for classifying actors
• boundary_criteria: Criteria for defining network boundaries
• methodology: 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