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Datasets:
Writerslogic
/
scholawrite-augmented

Tasks:
Text Classification
Text Generation
Languages:
English
Size:
100K<n<1M
Tags:
writing-process
revision-tracking
process-integrity
scholarly-writing
License:
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metadata 
license: apache-2.0
task_categories:
  - text-classification
  - text-generation
language:
  - en
tags:
  - writing-process
  - revision-tracking
  - process-integrity
  - scholarly-writing
size_categories:
  - 100K<n<1M
source_datasets:
  - minnesotanlp/scholawrite

ScholaWrite-Augmented

Process Integrity Benchmarks for Revision-Tracked Scholarly Writing

Dataset Description

ScholaWrite-Augmented is a revision-tracked scholarly writing dataset with annotated external insertion events, designed for process-integrity research. It augments the ScholaWrite seed dataset with synthetic injections at multiple sophistication levels, models boundary erosion over revision trajectories, and provides span-level annotations with explicit ambiguity flags.

This dataset does NOT label origin (human vs AI). Instead, it studies whether the observable revision process is consistent with iterative human authorship, or whether it exhibits evidence of process-inconsistent external insertion.

Dataset Summary

Statistic Value
Documents 5
Total Revisions 126,246
Injections 237
Trajectory States COLD (50%), WARM (33%), ASSIMILATED (16%)

Key Concepts

Concept Description
Injection Level Sophistication of insertion: naive (no context), topical (domain only), contextual (full local context)
Trajectory State Boundary erosion over revisions: COLD (unedited), WARM (partial integration), ASSIMILATED (full integration)
Ambiguity Flag Confidence in boundary determination: NONE, LOW, MEDIUM, HIGH
Causal Signatures Process metrics: repair locality, resource coupling, biometric plausibility

Dataset Structure 

data/
├── documents.jsonl      # Augmented revisions with metadata
├── annotations.jsonl    # Span-level insertion annotations
├── anomalies.jsonl      # Process-anomaly negative controls
└── stats.json           # Dataset statistics

Data Fields

documents.jsonl:

  • doc_id: Document identifier
  • revision_id: Unique revision identifier
  • revision_index: Sequential revision number
  • text: Normalized revision text
  • timestamp: Revision timestamp (if available)
  • provenance_hash: Content hash for verification
  • before_text: Text before this revision
  • writing_intention: Expert-annotated writing intention (15 classes)

annotations.jsonl:

  • doc_id, revision_id: Location identifiers
  • injection_id: Unique injection identifier
  • span_start_char, span_end_char: Character offsets
  • span_start_sentence, span_end_sentence: Sentence offsets
  • injection_level: naive | topical | contextual
  • trajectory_state: COLD | WARM | ASSIMILATED
  • ambiguity_flag: NONE | LOW | MEDIUM | HIGH
  • generator_class: Generator type (weak/mid/strong)
  • causal_trace: Full causal event trace

Usage

Loading the Dataset 

from datasets import load_dataset

dataset = load_dataset("Writerslogic/scholawrite-augmented")

# Access documents
for doc in dataset["train"]:
    print(doc["doc_id"], doc["text"][:100])

Loading Raw Files 

import json

# Load augmented documents
with open("data/documents.jsonl") as f:
    documents = [json.loads(line) for line in f]

# Load span-level annotations
with open("data/annotations.jsonl") as f:
    annotations = [json.loads(line) for line in f]

Intended Use

  • Research on process integrity and revision-trace analysis
  • Benchmarking methods that surface revision discontinuities
  • Studying boundary erosion and ambiguity in hybrid writing
  • Evaluating causal signatures for process verification

Non-Use / Out-of-Scope

This dataset MUST NOT be used for:

  • Punitive or automated enforcement decisions
  • Sole determination of authorship or tool use
  • High-stakes decisions without human review
  • Reverse identification of authors, papers, or institutions
  • Surveillance of writers without consent
  • Any framing as "AI detection"

Ethical Considerations

This dataset is designed to study process integrity, not to enable surveillance or enforcement. Key principles:

  1. No origin labels: We do not label text as "human" or "AI"
  2. Process over product: We assess revision traces, not final text properties
  3. Ambiguity is valid: Indeterminate cases are expected, not errors
  4. Assistance is compatible: Legitimate tool use with iterative integration is process-consistent

See ETHICS.md for full ethical considerations.

Terms and Attribution

This dataset is derived from ScholaWrite (MinnesotaNLP). Per the seed dataset terms:

  • No reverse identification of authors, papers, or institutions
  • No disclosure enabling identification
  • No PII introduction
  • Attribution to ScholaWrite required

Citation 

@inproceedings{wang2025scholawrite,
  title     = {ScholaWrite: A Writing Process Study Dataset for Scholarly Writing},
  author    = {Wang, Seulgi and Lee, Yoonna and Volkov, Ilya and Chau, Tuyen Luan and Kang, Dongyeop},
  booktitle = {Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics},
  year      = {2025}
}

@article{condrey2026scholawrite-augmented,
  title   = {Process Integrity Without Origin Labels: Cognitive Simulation for Hybrid Writing Verification},
  author  = {Condrey, David},
  journal = {ACL Findings},
  year    = {2026},
  url     = {https://github.com/writerslogic/scholawrite-augmented}
}

License

Apache-2.0 (inherited from ScholaWrite), with additional terms from the seed dataset regarding reverse identification and permitted use.