> ## Content Index
> Fetch the complete content index at: https://leventbulut.com/llms.txt
> Use this file to discover other available public pages before exploring further.

# Dataset v7: Detecting the Six Golden Rules — A Process Note
- URL: https://leventbulut.com/dataset-v7-detecting-the-six-golden-rules-a-process-note/
- Published: 2026-05-29T19:52:41.000Z
- Updated: 2026-07-26T15:39:42.000Z
- Description: Objective Projection v7, 6 altın kuralı açık bir Python boru hattıyla nasıl doğruluyor? Sınırları ve metodolojinin detaylarını keşfedin!
- Author: Levent Bulut
- Tags: Computational Narratology, Narrative Datasets, Objective Projection

*Version 7 of the Objective Projection dataset added three pieces that move the methodology from a claim into an auditable structure: a transparent detection pipeline that marks which rules each scene satisfies, machine-readable citation infrastructure, and full-text methodology papers. This note explains why we took this route — and which honest limits we declare openly.*

## The problem: the "I deleted the label, so I'm compliant" fallacy

[Objective Projection](https://leventbulut.com/objective-projection-the-engineering-of-writing-with-physical-parameters-instead-of-emotion-labels/) rests on six rules: Emotion Embargo, Simile Prohibition, Materialized Metaphors, Micro-Focus (Ng), Temporal Anchor, Atmosphere Contradiction. It is easy for a dataset to *say* it follows these rules; it is hard to *prove* it. Until v7, whether each of the 500 scenes obeyed a given rule was left to the reader's eye and trust. For an academic record, that is not enough.

## The solution: a deterministic, rule-based, open detection pipeline

In v7, every scene gained an `applied_rules` block. The thing that produces this block is **not** a language model (no LLM-as-judge) — it is `apply_rules.py`, a single-file, dependency-free Python script using word-boundary matching. We chose this deliberately:

- **Reproducible.** Clone the repo, run the script, get byte-identical output. Unlike an LLM judge, the result does not drift.
- **Auditable.** Exactly which patterns each rule checks for is written plainly in the script. A researcher can inspect a specific decision, change thresholds, or contest a call.
- **Transparent.** No black box. The full detection logic is readable.

Each `applied_rules` block carries six boolean flags, an `active_count`, `primary_rule`, `detection_method`, and `doctrine_version`. No existing field was modified, renamed, or removed — only added.

## An honest limit, declared: the rules are not equally reliable

This is the most important part of the release, and the thing most datasets hide. A rule-based checker is, by design, a blunt instrument. Detection rates on the target outputs vary by rule:

- **High reliability (95%+):** Simile Prohibition and Emotion Embargo — deterministic lexicon matching.
- **Moderate (60–80%):** Temporal Anchor, Materialized Metaphors, Micro-Focus — structural/heuristic patterns.
- **Deliberately conservative (\~10%):** Atmosphere Contradiction.

That last line is intentional. Atmosphere Contradiction encodes a semantic authorial choice that regex cannot reliably see. So we tuned the pipeline to favour **false negatives over false positives**: it would rather miss the rule than wrongly claim it. The reason is simple — keeping the dataset's positive labels trustworthy matters more than coverage. To a peer reviewer, this is not a weakness; it is a signal of strength.

## The citation infrastructure that ships with it

- `**CITATION.cff**` — Citation File Format v1.2.0\. Hugging Face, GitHub, and Zenodo recognise this file and surface a "Cite this dataset" affordance automatically. It carries the primary HF DOI (`10.57967/hf/8960`) and the Zenodo archive DOI (`10.5281/zenodo.19511369`), plus cross-references to the architectural framework and the Sₙ pilot report.
- **Two full-text papers under `academic/`:** the short-form methodology paper (*Beyond the Cortical Label*) and the Sₙ pilot report (`10.5281/zenodo.20362901`).

## In short

v7 was not about adding scenes. It was about making the existing 500 scenes **provable**. If you want a methodology to take its own claim — "literature is not a feeling, it is a physics" — seriously, that claim has to be auditable, reproducible, and honestly bounded. That is what v7 aimed at.

*Dataset:* [*huggingface.co/datasets/leventbulut/objective-projection*](https://huggingface.co/datasets/leventbulut/objective-projection?ref=leventbulut.com) *· DOI: 10.57967/hf/8960 · Full technical write-up:* [*huggingface.co/blog/leventbulut/objective-projection*](https://huggingface.co/blog/leventbulut/objective-projection?ref=leventbulut.com)

[*Levent Bulut*](https://leventbulut.com/)