Moral Stories Annotation

# Moral Stories Annotation
# Based on Emelin et al., EMNLP 2021
# Paper: https://aclanthology.org/2021.emnlp-main.54/
# Dataset: https://huggingface.co/datasets/demelin/moral_stories
#
# Moral Stories contains structured narratives for moral reasoning.
# Each story has seven components:
# 1. Norm: A social guideline (e.g., "It's good to help others")
# 2. Situation: The context and setting
# 3. Intention: What the actor wants to achieve
# 4. Moral action: An action that follows the norm
# 5. Moral consequence: Positive outcome of moral action
# 6. Immoral action: An action that violates the norm
# 7. Immoral consequence: Negative outcome of immoral action
#
# Annotation Guidelines:
# 1. Consider the NORM as the ethical standard
# 2. Evaluate actions against this norm
# 3. A moral action achieves the intention while respecting the norm
# 4. An immoral action achieves the intention but violates the norm
# 5. Consider both intent and method when evaluating
#
# Key Questions:
# - Does the action respect the stated norm?
# - Would most people consider this action acceptable?
# - Does the actor consider others' well-being?

port: 8000
server_name: localhost
task_name: "Moral Stories Annotation"

data_files:
  - sample-data.json
id_key: id
text_key: scenario

output_file: annotations.json

annotation_schemes:
  # Step 1: Action classification
  - annotation_type: radio
    name: action_morality
    description: "Does the described ACTION adhere to or violate the NORM?"
    labels:
      - "Moral (follows norm)"
      - "Immoral (violates norm)"
      - "Neutral/unclear"
    tooltips:
      "Moral (follows norm)": "The action achieves the goal while respecting the ethical norm"
      "Immoral (violates norm)": "The action achieves the goal but violates the ethical norm"
      "Neutral/unclear": "The action's morality is ambiguous or doesn't clearly relate to the norm"

  # Step 2: Consequence prediction
  - annotation_type: radio
    name: likely_consequence
    description: "What is the likely consequence of this action?"
    labels:
      - "Positive for all"
      - "Positive for actor, negative for others"
      - "Negative for actor"
      - "Negative for all"
      - "Neutral/mixed"
    tooltips:
      "Positive for all": "The action benefits everyone involved"
      "Positive for actor, negative for others": "The actor gains but others suffer"
      "Negative for actor": "The action backfires on the actor"
      "Negative for all": "Everyone is worse off"
      "Neutral/mixed": "Consequences are mixed or unclear"

  # Step 3: Norm applicability
  - annotation_type: radio
    name: norm_applicability
    description: "How applicable is the stated norm to this situation?"
    labels:
      - "Highly applicable"
      - "Somewhat applicable"
      - "Not very applicable"
      - "Not applicable"
    tooltips:
      "Highly applicable": "The norm directly addresses this situation"
      "Somewhat applicable": "The norm is relevant but not perfectly matched"
      "Not very applicable": "The norm only loosely relates to the situation"
      "Not applicable": "The norm doesn't really apply here"

  # Step 4: Agreement with norm
  - annotation_type: likert
    name: norm_agreement
    description: "Do you personally agree that this norm is a good ethical guideline?"
    min_value: 1
    max_value: 5
    labels:
      1: "Strongly disagree"
      2: "Disagree"
      3: "Neutral"
      4: "Agree"
      5: "Strongly agree"

allow_all_users: true
instances_per_annotator: 50
annotation_per_instance: 3
allow_skip: true
skip_reason_required: false