Methodology: How Loglisted Analyzes Screenplays
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Contents
1. Core Principles
1.1 Explainability
Scores should be supported by specific evidence from the screenplay.
A score without an explanation is incomplete.
Where practical, findings should identify the relevant:
page
scene
sequence
character
line of dialogue
structural event
recurring pattern
1.2 Separation of Observation and Judgment
The system should distinguish among:
Objective or directly observable information
Examples include page count, scene count, location, character appearances, and dialogue volume.Model inference
Examples include likely genre, tone, intended audience, thematic focus, and story engine.Evaluative judgment
Examples include whether dialogue is effective, whether pacing is strong, or whether a premise is commercially distinctive.
These forms of information should not be presented as interchangeable.
1.3 Blind Evaluation
The scoring process should exclude information that is not necessary to evaluate the screenplay itself.
The evaluator should not receive:
writer identity
demographic information
account information
profile information
prior submission history
previous scores
payment status
platform engagement history
industry relationships
personal risk or abuse classifications
The screenplay should be evaluated on the submitted work and the evidence available within it.
1.4 Deterministic Calculation
The language model may assign subcategory scores and provide evidence.
Deterministic code should:
validate score ranges
validate required fields
validate weight totals
calculate weighted category scores
calculate the overall score
apply rounding rules
store methodology and prompt versions
The model should not be responsible for performing final weighting arithmetic when deterministic code can perform it consistently.
1.5 Sparse Top Scores
The top of the scoring scale should remain intentionally rare.
A compelling premise, one excellent character, or one standout sequence is not enough to justify an elite overall score.
Scores above 9.0 should require sustained excellence across nearly every relevant scoring category.
1.6 Scores Are Diagnostic
Scores are intended to help identify strengths, weaknesses, revision priorities, and potentially promising work.
They should not be treated as:
objective measurements of artistic worth
guarantees of commercial success
guarantees of representation or production
rankings of writers as people
substitutes for human taste
permanent judgments
2. Scoring Architecture
The methodology contains ten top-level categories:
Premise
Story
Structure
Characters
Dialogue
Pacing
Theme
Tone
Marketability
Craft
Each category contains five subcategories.
Each subcategory receives a score from 1.0 to 10.0.
The five subcategory weights within a category must total 100%.
2.1 Category Score
A category score is calculated as the weighted sum of its five subcategory scores:
Category Score = (Subcategory 1 Score × Subcategory 1 Weight) + (Subcategory 2 Score × Subcategory 2 Weight) + (Subcategory 3 Score × Subcategory 3 Weight) + (Subcategory 4 Score × Subcategory 4 Weight) + (Subcategory 5 Score × Subcategory 5 Weight)
Weights are expressed as decimals during calculation.
For example, a weight of 20% is calculated as 0.20.
2.2 Overall Score
The overall score is the arithmetic mean of all ten category scores:
Overall Score = Sum of All Ten Category Scores ÷ 10
Each top-level category therefore contributes equally to the overall score in methodology version 0.1.0.
Changing the relative weight of a top-level category requires a public methodology proposal and a new methodology release.
2.3 Internal Precision and Public Rounding
Calculations should preserve their available internal precision.
Public category and overall scores should be rounded to one decimal place.
For example:
Internal score: 7.846
Public score: 7.8
Rounding should occur only after the underlying category or overall calculation is complete.
Intermediate values should not be repeatedly rounded in a way that changes the final result.
3. Score Scale
All subcategories, categories, and overall results use a scale from 1.0 to 10.0.
3.1 General Score Anchors
10.0 — Extraordinary Benchmark
One of the finest examples of screenplay execution in its genre or format.
A score of 10.0 should be exceptionally rare and supported by sustained evidence.
9.5–9.9 — Elite Professional
Elite professional work that can credibly be discussed alongside the strongest modern screenplays.
The work demonstrates sustained excellence with very few meaningful weaknesses.
9.0–9.4 — Outstanding Professional
Outstanding professional-level work comparable to top-tier produced screenplays.
Weaknesses may exist, but they do not materially undermine the execution.
8.0–8.9 — Strong Professional
Strong professional work with realistic commercial or artistic viability.
The screenplay demonstrates clear command of craft, although meaningful improvements may remain.
7.0–7.9 — Good
Good work with clear strengths and material weaknesses.
A score in this range should not be described as poor or failing.
A 7 represents successful execution with identifiable room for improvement.
6.0–6.9 — Competent and Promising
Competent work that demonstrates promise but is not yet consistently industry-ready.
The screenplay may contain strong elements but lacks consistency, depth, clarity, or execution in important areas.
Below 6.0 — Developing
Developing work that requires substantial revision.
The screenplay may contain promising ideas or isolated strengths, but important craft problems interfere with its effectiveness.
3.2 Interpretation of Key Scores
The scoring prompt should reinforce the following:
7 is good
8 is professional
9 is exceptional
Scores above 9.5 are extraordinarily rare
Evaluators should not inflate scores merely because a script is competent, enjoyable, polished, or promising.
Evaluators should also not treat a score below 8 as a dismissal of the writer or screenplay.
4. Scoring Categories
4.1 Premise
Premise evaluates the strength, clarity, distinctiveness, and dramatic potential of the screenplay’s central concept.
It considers whether the underlying idea creates a meaningful foundation for the story.
Current Subcategories and Weights
Originality 25%
Clarity 12.5%
Hook 25%
Stakes 12.5%
Commercial Appeal 25%
Total 100%
4.2 Story
Story evaluates the development, coherence, escalation, and dramatic effectiveness of the narrative.
It considers whether events build meaningfully and whether the central dramatic movement remains engaging and understandable.
Current Subcategories and Weights
Conflict 25%
Escalation 25%
Causality 15%
Emotional Impact 15%
Resolution 20%
Total 100%
4.3 Structure
Structure evaluates how effectively the screenplay organizes dramatic events, sequences, turning points, and progression.
It considers whether the form supports the intended experience rather than requiring every script to follow one universal formula.
Current Subcategories and Weights
Opening 25%
Plot Progression 25%
Turning Points 15%
Climax 25%
Scene Flow 10%
Total 100%
4.4 Characters
Characters evaluates characterization, motivation, agency, development, relationships, and dramatic function.
It considers whether characters feel sufficiently distinct, purposeful, and compelling within the intended style and genre.
Current Subcategories and Weights
Protagonist 25%
Supporting Characters 25%
Character Arcs 25%
Motivation 12.5%
Relationships 12.5%
Total 100%
4.5 Dialogue
Dialogue evaluates the effectiveness of spoken language within the screenplay’s intended genre, tone, period, and style.
It may consider voice, subtext, naturalness, memorability, dramatic purpose, and efficiency.
Dialogue should not be judged only by whether it resembles everyday speech. Stylized, heightened, sparse, period-specific, or genre-specific dialogue may be effective on its own terms.
Current Subcategories and Weights
Naturalness 15%
Subtext 10%
Voice 40%
Memorability 25%
Efficiency 10%
Total 100%
4.6 Pacing
Pacing evaluates the screenplay’s control of momentum, duration, escalation, variation, and narrative movement.
It considers whether the pace supports the intended dramatic experience.
Fast pacing is not automatically better than slow pacing. Deliberate stillness, tension, repetition, or gradual development may be appropriate when effectively executed.
Current Subcategories and Weights
Momentum 25%
Scene Rhythm 15%
Narrative Balance 20%
Tension Management 20%
Engagement 20%
Total 100%
4.7 Theme
Theme evaluates the screenplay’s development of ideas, meaning, moral or emotional questions, and thematic coherence.
A screenplay does not need to state its themes explicitly.
Theme should be evaluated through the relationship among events, characters, choices, imagery, conflict, and consequences.
Current Subcategories and Weights
Novelty 25%
Clarity 25%
Integration 20%
Depth 15%
Consistency 15%
Total 100%
4.8 Tone
Tone evaluates the screenplay’s control of mood, style, emotional register, genre expectations, and tonal consistency.
Tonal shifts are not automatically weaknesses.
A shift should be evaluated according to whether it appears intentional, understandable, and effective.
Current Subcategories and Weights
Consistency 25%
Genre Alignment 10%
Emotional Authenticity 20%
Atmosphere 35%
Relatability 10%
Total 100%
4.9 Marketability
Marketability evaluates the screenplay’s practical positioning, audience clarity, production considerations, genre accessibility, and potential industry interest.
Marketability is not the same as artistic quality.
A highly marketable screenplay may contain craft weaknesses. A less conventionally marketable screenplay may demonstrate exceptional artistic achievement.
Marketability should not dominate unrelated craft categories.
Current Subcategories and Weights
Audience Appeal 50%
General Positioning 20%
roduction Feasibility 10%
Distinctiveness 10%
Franchise Potential 10%
Total 100%
4.10 Craft
Craft evaluates the technical execution, clarity, readability, formatting, visual communication, and control demonstrated on the page.
Craft should distinguish between minor formatting imperfections and problems that materially interfere with comprehension or dramatic impact.
Current Subcategories and Weights
Formatting 25%
Grammar 25%
Visual Storytelling 20%
Clarity of Writing 20%
Economy 10%
Total 100%
5. Evidence Requirements
Each scored subcategory should be supported by evidence.
Evidence may include:
a specific page or scene
a line or exchange of dialogue
a recurring narrative pattern
a structural event
a character decision
a setup and payoff
an example of escalation
an example of tonal control or inconsistency
an example of visual storytelling
an example of unclear or ineffective execution
Evidence should explain why it supports the score.
A citation without interpretation is incomplete.
An interpretation without identifiable evidence should be treated with lower confidence.
5.1 Representative Evidence
The evaluator should select evidence that represents meaningful patterns rather than relying only on isolated moments.
A single weak line should not determine the entire dialogue score.
A single strong scene should not justify an elite character or structure score if the rest of the screenplay does not sustain that quality.
5.2 Positive and Negative Evidence
Where practical, analysis should identify both:
evidence supporting the screenplay’s strengths
evidence supporting identified weaknesses or revision priorities
5.3 Evidence Availability
When the screenplay does not provide enough evidence to judge a criterion reliably, the evaluator should report insufficient evidence or reduced confidence rather than inventing certainty.
6. Screenplay Processing
Long screenplays may be processed in sections before final scoring.
6.1 Chunk Summaries
When a screenplay cannot be evaluated in a single context, sections may be summarized.
Chunk summaries should preserve information relevant to later scoring, including:
important events
character actions and decisions
conflict
relationships
setups and payoffs
changes in goals
tonal developments
thematic developments
pacing changes
notable dialogue patterns
page- or scene-level evidence
Chunk summaries should avoid replacing evidence with unsupported conclusions.
6.2 Final Scoring Context
The final evaluator may receive:
screenplay text or selected portions
chunk summaries
representative excerpts
deterministic metadata
relevant page or scene references
the current rubric
scoring anchors
output requirements
methodology and prompt versions
The evaluator should not receive writer identity, account history, previous scores, or unrelated platform data.
6.3 Representative Excerpts
Representative excerpts should be selected to preserve evidence relevant to scoring.
Selection should not intentionally favor only the strongest or weakest portions.
The process should seek coverage across:
the beginning
the middle
the ending
major turning points
important character interactions
significant tonal or pacing changes
scenes relevant to disputed or uncertain dimensions
7. Confidence and Uncertainty
A score should not imply more certainty than the available evidence supports.
Confidence may be reduced when:
the available text is incomplete
parsing is unreliable
scenes are missing
formatting is ambiguous
the screenplay uses an unusual or experimental form
a criterion has limited supporting evidence
different portions of the screenplay strongly conflict
repeated evaluations vary substantially
genre or format classification is uncertain
Confidence should be reported separately from quality.
A high score with low confidence is not equivalent to a high score with strong evidence and stable evaluation.
A low-confidence result should not be presented as definitive.
8. Benchmarking and Percentiles
Raw scores and percentile rankings are different measures.
A raw score reflects evaluation under the published rubric.
A percentile reflects the script’s position within a defined comparison cohort.
Percentiles should identify the relevant cohort, including factors such as:
methodology version
screenplay format
genre, when applicable
evaluation period
sample size
inclusion criteria
8.1 Illustrative Launch Assumptions
The initial white paper proposed an illustrative emerging-writer distribution with:
mean overall score near 6.3
median overall score near 6.2
standard deviation near 1.0
approximately 10% of submissions at 7.6 or higher
approximately 4% of submissions at 8.0 or higher
approximately 1% of submissions at 8.7 or higher
These values are modeling assumptions for planning.
They are not observed industry statistics and should not be presented as empirical facts.
They should be replaced or revised when sufficient platform data and independent validation become available.
8.2 Modeled Versus Observed Benchmarks
Every published benchmark should be labeled as one of the following:
Modeled assumption
Observed platform distribution
Curated evaluation benchmark
External comparison dataset
Observed benchmark claims should include:
cohort definition
sample size
methodology version
collection period
known exclusions
relevant uncertainty
whether the scripts were independently selected
8.3 Genre Benchmarking
Genre may affect the interpretation of pacing, dialogue, tone, structure, exposition, audience expectations, and production considerations.
Genre benchmarking should not automatically redefine artistic quality.
The methodology should distinguish among:
criteria intended to apply broadly
criteria whose interpretation may depend on genre or format
descriptive genre conventions that should not automatically affect quality scores
Hybrid, experimental, and cross-genre work should not be forced into a single comparison group when that would distort the evaluation.
9. Discovery and Ranking
The overall score should not be the sole basis for discovery or industry recommendation.
Discovery may also consider:
standout category scores
genre
format
intended audience
production scale
tone
story engine
creative fit
specific industry search criteria
human review
confidence in the analysis
Similarity should not be presented as quality.
A script that resembles another work in genre, tone, character configuration, or pacing is not necessarily equally strong.
Any similarity or creative-neighbor result should explain which dimensions contributed to the match.
10. Validation
The methodology should be treated as a versioned measurement program rather than a static prompt.
10.1 Blind Validation
Scripts should be evaluated without writer identity or career information.
10.2 Inter-Rater Evaluation
Model scores should be compared with assessments from multiple qualified human readers where possible.
Human disagreement should be measured rather than hidden.
10.3 Repeatability
A fixed evaluation set should be rescored after meaningful changes to:
prompts
models
rubric definitions
weights
chunking
excerpt selection
schemas
aggregation rules
10.4 Calibration
Score bands should be tested against:
independent reader judgments
consistency across evaluators
professional read requests
positive coverage or recommendation decisions
meetings
representation interest
options, staffing, or production outcomes where available
These outcomes may inform validation but should not be treated as pure measurements of screenplay quality.
10.5 Precision at the Top of the Funnel
One important discovery measure is precision among highly ranked or spotlighted scripts.
For example:
Of the scripts identified as especially promising, what proportion receive serious reads or positive judgments from experienced industry readers?
The methodology does not need to create a perfect ranking of every screenplay to be useful.
It should be dependable where consequential discovery decisions are made.
10.6 Bias Review
Evaluation should test for unintended disparities related to:
genre
format
culture
language variety
writing style
nonlinear structure
experimental form
demographic representation
historical period
production scale
conventional versus unconventional storytelling
A difference in score is not automatically evidence of bias, but meaningful and repeated disparities should be investigated.
11. Model and Prompt Variance
Language-model evaluation may vary because of:
model architecture
model version
prompt version
context limits
generation settings
chunking
summarization
excerpt selection
parsing
randomness
provider changes
Every result should retain enough version information to identify the method used.
At minimum, a result should record:
{ "methodologyVersion": "0.1.0",
"rubricVersion": "rubric-v1",
"scoringPromptVersion": "scoring-v1",
"chunkSummaryPromptVersion": "chunk-summary-v1",
"benchmarkVersion": "benchmark-v1", "model": "<model-id>" }
Historical scores should not be silently rewritten after a methodology change.
When recalculation is possible, the original result and version should remain identifiable.
12. Known Limitations
This methodology has important limitations.
12.1 Artistic Judgment Is Not Fully Objective
Reasonable readers may disagree about quality, meaning, tone, originality, marketability, or effectiveness.
A structured rubric can make disagreement easier to inspect, but it cannot eliminate taste.
12.2 Models May Miss Context
A model may misunderstand:
irony
cultural context
period-specific language
genre conventions
visual implication
experimental structure
intentional ambiguity
subtle character behavior
humor
subtext
12.3 Summarization Can Remove Evidence
When long screenplays are summarized, details relevant to scoring may be compressed or lost.
The final score may therefore be affected by what survives the summarization and excerpt-selection process.
12.4 Marketability Is Time-Sensitive
Market conditions change.
A marketability score may depend on assumptions about audience demand, production costs, industry trends, and buyer preferences that can become outdated.
12.5 Benchmarks May Be Incomplete
Early benchmark distributions may be modeled rather than observed.
Even observed platform data may not represent all emerging writers, genres, cultures, markets, or submission channels.
12.6 High Scores Do Not Guarantee Outcomes
A high score does not guarantee:
representation
financing
production
awards
commercial performance
audience reception
12.7 Low Scores Do Not Determine Potential
A low score may reflect:
an unusual creative choice
insufficient evidence
a limitation of the methodology
model error
a mismatch between the work and the evaluation framework
Scores should support revision and discovery, not close off opportunity.
13. Versioning
This methodology follows semantic versioning where practical.
Patch Release
Examples:
typographical corrections
wording clarification
documentation improvements
changes that cannot affect scores
Example:
0.1.0 → 0.1.1
Minor Release
Examples:
compatible additions
clarified scoring anchors that may affect interpretation
recalibration
new optional fields
revised benchmark assumptions
changes that may affect scores without replacing the overall framework
Example:
0.1.0 → 0.2.0
Major Release
Examples:
adding or removing a scoring category
changing the 1–10 scale
changing the overall-score formula
changing category-weight architecture
incompatible output-schema changes
redesigning the methodology
Example:
0.2.0 → 1.0.0
Every release that may affect results should explain:
what changed
why it changed
which scores may be affected
whether prior results remain comparable
what evaluation supports the change
Changes should also be recorded in CHANGELOG.md on GitHub.
14. Proposing Changes
Contributors are encouraged to challenge:
category definitions
subcategories
weights
score anchors
evidence requirements
confidence rules
benchmark assumptions
aggregation methods
genre handling
validation procedures
known limitations
A methodology proposal should include:
the problem
the current behavior
the proposed change
the reasoning
supporting evidence or examples
expected scoring impact
drawbacks and tradeoffs
a proposed evaluation method
compatibility implications
A proposed change should explain not only why it may improve the methodology, but also where it may fail.
See CONTRIBUTING.md and GOVERNANCE.md on GitHub for the review and decision processes.
15. Current Release Status
Version 0.1.0 is a draft for public comment.
It should not be described as an established industry standard.
The long-term goal is to build a methodology that may earn broader trust through:
public scrutiny
transparent changes
independent evaluation
professional-reader comparison
genre- and format-specific validation
bias review
reproducible releases
demonstrated discovery outcomes
Trust should be earned through evidence and outcomes rather than asserted by the project.