# Analysis

Every number and table in the paper is produced by a script in this
folder, reading only the data shipped in `data/` and `stimuli/`. Run
everything from the repository root.

```
analysis/
├── common/                  shared helpers (imported, never run directly)
├── 01_data_prep/            raw exports → analysis tables (only for new collections)
├── 02_main_study/           the human study (Tables 1, 2, 5, 21)
├── 03_synthetic_raters/     AI-rater alignment (Tables 3, 4, 6–8)
├── 04_annotation_budget/    using AI ratings to allocate human raters (Table 20)
├── 05_prompt_analyses/      which prompts swing across modality (Table 19, Appendix J)
├── 06_voice_study/          Ablation A: four TTS voices (Tables 9, 10, 12; Appendix G)
├── 07_audio_context_study/  Ablation B: conversation history heard as audio (Table 13; Appendix H)
├── 08_cross_study/          contrasts across arms of both ablations (Tables 11, 14–18)
├── run_all.sh               runs every script below in order
└── Stimuli_Sampling.pdf     stimulus-screening worksheet (reference only)
```

Scripts write tables to `results/<folder>/` and figures to `figures/`.
Inputs in `data/` are never overwritten.

## Setup

The analyses need Python and R together, so the simplest setup is
[pixi](https://pixi.sh), which installs both from `pixi.toml` and `pixi.lock`:

```bash
pixi install
pixi run python analysis/02_main_study/01_omnibus_bias_model.py
```

To use `pip` instead, install R (≥ 4.2) yourself along with the R packages
that `pymer4` and the analyses load:

```bash
python3.11 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
brew install r     # or your platform's R installer
R -e 'install.packages(c("lme4","lmerTest","emmeans","broom","broom.mixed","report","parameters","performance","insight","tidyverse","arrow","boot"), repos="https://cloud.r-project.org")'
```

Results were verified with Python 3.11.7, R 4.5.2, lme4 1.1-38,
lmerTest 3.1-3 and emmeans 2.0.0. `08_cross_study/09_validate_lme4_substitution.py`
checks that the direct `lme4` bridge used by the follow-up analyses
reproduces the paper's Table 1 before you rely on it.

## Where each result comes from

The synthetic-rater scripts take `--model gpt4o | gemini_36_flash |
qwen35_omni_flash`. The default, `gpt4o`, gives the main-text Tables 3–4;
running all three gives the cross-family Tables 6–8. The voice study is
**Ablation A** (arms A1 am_michael, A2 Chatterbox, A3 Serena) and the
audio-context study is **Ablation B** (B1 one-voice history, B2 two-voice
history). In Tables 10–18 the text and audio reference rows are the main
study's conditions recomputed on each ablation's 30 prompts.

### Human study and main-text AI comparisons

| Result | Script |
|---|---|
| Table 1: omnibus bias model; per-modality length effects | `02_main_study/01_omnibus_bias_model.py` |
| Table 2: ICC and Krippendorff's α, bootstrap CIs, permutation tests | `02_main_study/02_reliability_icc_alpha.py` (slow, see below) |
| ICC(2,k) by number of raters (figure) | `02_main_study/03_icc_by_k_curve.py` |
| Decision threshold (audio M = 27.9, text M = 41.7); cross-modality winner agreement (53/100, p = .31); agreement-by-decisiveness figures | `02_main_study/04_cross_modality_agreement.py` |
| Prompt-specific modality effects (random-slope model, χ²(2) = 22.1) | `02_main_study/05_prompt_modality_effects.py` |
| Practice effects on trial duration | `02_main_study/06_practice_effects.py` |
| Table 5: session and trial characteristics by condition | `02_main_study/07_session_characteristics.py` |
| Table 21: participant demographics by condition | `02_main_study/08_participant_demographics.py` |
| Qualitative TF-IDF analysis of justifications | `02_main_study/09_qualitative_tfidf.py` |
| Robustness: education covariate | `02_main_study/10_robust_education.py` |
| Robustness: audio-quality covariate | `02_main_study/11_robust_audio_quality.py` |
| Robustness: relistening to the first clip | `02_main_study/12_robust_relisten.py` |
| Robustness: ceiling compression | `02_main_study/13_robust_ceiling.py` |
| Table 3: AI–human alignment; PRISM vs. external raters | `03_synthetic_raters/01_ai_human_alignment.py` |
| Table 4: AI differentiation predicts human agreement | `03_synthetic_raters/02_ai_predicts_agreement.py` |

`04_cross_modality_agreement.py` is the authoritative source for the
53/100 winner-agreement figure. `05_prompt_modality_effects.py`
deliberately prints no winner-agreement number, because its
sign-of-mean rule differs from `04` in edge cases.

### Additional synthetic raters, triage and prompt analyses (Appendices F, J, K)

| Result | Script |
|---|---|
| Table 6: AI–human agreement and rating error, three rater families | `03_synthetic_raters/01_ai_human_alignment.py --model <m>` |
| Table 6 follow-up: calibration offset vs. rank agreement (centered MAE) | `03_synthetic_raters/05_mae_bias_decomposition.py` |
| Table 7: AI differentiation predicts human agreement, three families | `03_synthetic_raters/02_ai_predicts_agreement.py --model <m>` |
| Table 8: PRISM vs. AI annotators across raters | `03_synthetic_raters/01_ai_human_alignment.py --model <m>` (writes `h2_prism_comparisons*.csv`) |
| Cross-family judge agreement, text vs. audio | `03_synthetic_raters/03_cross_model_agreement.py --models gpt4o gemini_36_flash qwen35_omni_flash` |
| Do AI judges reproduce the human order and length biases? | `03_synthetic_raters/04_synth_bias_effects.py` |
| Triage experiment: gap-threshold sweep, budget-matched allocation | `04_annotation_budget/01_triage_experiment.py --model <m>` |
| Human raters needed per AI-gap band | `04_annotation_budget/02_human_sample_size_by_ai_gap.py --model <m>` |
| Table 20: first-hit vs. stable ratings by gap band | `04_annotation_budget/03_raters_to_gold_by_gap.py --model <m>` |
| Tiered vs. uniform allocation at matched budget | `04_annotation_budget/04_triage_allocation_table.py` |
| Cost per trial behind the triage savings ($0.26 text, $0.46 audio) | `04_annotation_budget/05_annotation_cost_per_trial.py` |
| Length and conversation-structure predictors of the modality effect (Appendix J) | `05_prompt_analyses/01_modality_effect_features.py` |
| Table 19: modality effect by induced prompt type; inter-coder κ = .699 | `05_prompt_analyses/05_topic_code_stimuli.py --reuse` |
| Context-dependent prompts flip winners more often (68% vs. 35%; Section 5.2) | `05_prompt_analyses/06_context_dependence_flips.py` |
| Qualitative fail cases: prompts ranked by human cross-modal gap, with justifications | `05_prompt_analyses/02_qual_rating_disagreement.py`, `03_qual_raw_rating_diff.py`, `04_qual_pair_diff_table.py` |

`05_topic_code_stimuli.py --reuse` rebuilds Table 19 from the shipped
topic codes (`data/main_study/stimulus_topic_codes*.csv`) and makes no
network calls. Without `--reuse` it re-runs the topic induction and
coding through the Gemini and OpenAI APIs, which needs API keys, costs
money, and will not return exactly the same codes.

### Voice and audio-context ablations (Appendices G–I)

| Result | Script |
|---|---|
| Table 9: acoustic characterization (WPM, f0, loudness) | `06_voice_study/03_acoustics.py` (needs the WAVs) |
| Voice f0 level and self-consistency (drift 0.27–0.98 st) | `06_voice_study/04_f0_drift.py` (needs the WAVs) |
| Table 10: modality signatures by voice. Threshold and ICC columns come from the threshold summary; recency and length come from the replication script | `08_cross_study/01_threshold_summary.py`, `06_voice_study/05_replication.py` |
| Tables 11 and 14: equivalence tests (TOST) between arms | `08_cross_study/03_equivalence_tost.py` (run `02` first) |
| Table 12: synthetic-rating alignment by voice | `06_voice_study/06_synth_alignment.py` |
| Table 12 follow-up: prompt-clustered bootstrap CIs | `06_voice_study/07_synth_alignment_bootstrap.py` (5,000 reps) |
| Table 13: audio-context modality signatures | `08_cross_study/01_threshold_summary.py`, `07_audio_context_study/02_replication.py` |
| Table 15: winner agreement with a split-half noise floor | `08_cross_study/04_winner_floor.py` |
| Table 16: rating-scale usage by condition | `08_cross_study/05_scale_usage.py` |
| Tie probability by condition (companion to Table 16) | `08_cross_study/06_tie_probability.py` |
| Table 17: estimated marginal means for the decision threshold | `08_cross_study/02_condition_contrasts.py` |
| Table 18: pairwise contrasts between conditions | `08_cross_study/02_condition_contrasts.py` |
| Arm × position and arm × length interactions (p = .15, p = .003) | `08_cross_study/07_bias_interactions.py` |
| History depth, trial position, session duration, completion rates | `08_cross_study/08_feasibility_covariates.py` |
| B1 split at median trial duration (slower half threshold 19.6) | `07_audio_context_study/03_duration_split.py` |
| Audio-quality ratings and transcript divergence by voice (Serena −18.5) | `06_voice_study/09_quality_by_voice.py` |
| AI-judge winner agreement across voices | `06_voice_study/08_judge_winner_agreement.py --model <m>` |
| Transcript consistency of each voice against the baseline | `06_voice_study/02_transcript_consistency.py` |
| Prompt selection for each study | `06_voice_study/01_select_prompts.py`, `07_audio_context_study/01_select_prompts.py` |

The prompt-selection scripts write their reselection to `results/` and
never overwrite the fielded prompt lists in `stimuli/*/prompts.csv`.

Two values print slightly differently from the paper, which was rounded
from an earlier pass: the f0 drift script prints .694/.973 st for Serena
and Chatterbox (paper: 0.70/0.98), and the Table 15 split-half floors
print .544/.565/.547/.557 (paper: .543/.564/.548/.555). Conclusions are
unchanged.

## Running everything

`pixi run bash analysis/run_all.sh` runs the commands below in order:

```bash
M="gpt4o gemini_36_flash qwen35_omni_flash"

for s in 01 03 04 05 06 07 08 09 10 11 12 13; do python analysis/02_main_study/${s}_*.py; done
python analysis/02_main_study/02_reliability_icc_alpha.py          # ~3 hours

for m in $M; do
  python analysis/03_synthetic_raters/01_ai_human_alignment.py --model $m
  python analysis/03_synthetic_raters/02_ai_predicts_agreement.py --model $m
  for s in 01 02 03; do python analysis/04_annotation_budget/${s}_*.py --model $m; done
done
python analysis/03_synthetic_raters/03_cross_model_agreement.py --models $M
for s in 04 05; do python analysis/03_synthetic_raters/${s}_*.py; done
for s in 04 05; do python analysis/04_annotation_budget/${s}_*.py; done

for s in 01 02 03 04 06; do python analysis/05_prompt_analyses/${s}_*.py; done
python analysis/05_prompt_analyses/05_topic_code_stimuli.py --reuse

for s in 01 02 05 06 07 09; do python analysis/06_voice_study/${s}_*.py; done
for m in gemini_36_flash qwen35_omni_flash; do
  python analysis/06_voice_study/08_judge_winner_agreement.py --model $m
done
for s in 01 02 03; do python analysis/07_audio_context_study/${s}_*.py; done
for s in 01 02 03 04 05 06 07 08 09; do python analysis/08_cross_study/${s}_*.py; done
```

Runtime and dependencies:

- **Reliability (`02_main_study/02`)** takes about three hours, from the
  bootstrap and permutation tests. It checkpoints to
  `_internal/run_outputs/` and resumes after an interruption. Delete that
  folder to recompute from scratch.
- **Replication scripts (`06/05`, `07/02`)** accept `--skip-models` for a
  fast descriptive pass.
- **Acoustics (`06_voice_study/03`, about 30 minutes) and f0 drift (`04`)**
  read the rendered WAVs in `stimuli/voice_study/audio/` (see
  `stimuli/voice_study/README.md`). Their outputs ship in
  `stimuli/voice_study/`, so you can skip them. `03_acoustics.py --out <path>`
  writes elsewhere instead of overwriting the shipped table.

## Running on your own data

The main-study scripts (`02_main_study/*`, `03_synthetic_raters/01`, `02`)
read from `data/main_study/` by default. Point them at another folder
with an environment variable or a flag:

```bash
ANALYSIS_DATA_DIR=data/your_study python analysis/02_main_study/01_omnibus_bias_model.py
```

To build that folder from a new collection:

```bash
node tools/export-firestore.js                      # → data/your_study/raw/firebase_export.json
ANALYSIS_DATA_DIR=data/your_study python analysis/01_data_prep/01_build_stim_tables.py
ANALYSIS_DATA_DIR=data/your_study python analysis/01_data_prep/02_convert_main_json_to_csv.py
```

The convert script refuses to run against `data/main_study/`. The
remaining folders (`03`–`08`) read the shipped follow-up and
synthetic-rating tables directly, so they don't take a data folder.

The data-prep scripts are only needed for new collections; the shipped
tables are their outputs:

| Script | Produces |
|---|---|
| `01_data_prep/01_build_stim_tables.py` | `prompt_stim_table.csv`, `survey_long_format.csv` from a `survey_data.json` |
| `01_data_prep/02_convert_main_json_to_csv.py` | main-study long, wide and participant tables from a Firestore export |
| `01_data_prep/03_synth_to_long_csv_multi.py` | `synth_long_*` tables from a wide synthetic-ratings file (`--model`, `--input`) |
| `01_data_prep/04_convert_followup_json_to_csv.py` | follow-up long tables from `data/<study>/raw/` exports (see `tools/export-firestore-followup.js`) |

## Stimulus-screening worksheet

`Stimuli_Sampling.pdf` is a 39-page export of the sheet used to screen
candidate PRISM turns before the 200 study clips were chosen: yellow =
poor audio, orange = other language, green = empty output, red =
harmful. It is reference material only. The machine-readable provenance
is `stimuli/main_study/study_stimuli_mapping.csv`.