# Data dictionary: `data/main_study/` Anonymized data from the main paired-preference study: 94 analyzed participants (48 text, 46 audio) rating 100 PRISM prompt pairs, 20 per participant. ParticipantIDs are remapped from Firebase anonymous UIDs to `P001..P094`. No Prolific IDs, timestamps, or session metadata that could re-identify a participant are included, and every justification was manually reviewed before release. ## Human ratings ### `participant_table_clean.csv` One row per analyzed participant (n = 94). | column | type | description | |---|---|---| | ParticipantID | str | `P001`..`P094` | | Modality | str | `text` or `audio` | | SurveyCompleted | bool | reached the post-survey form | | assignedTrialIDs | str | comma-separated PromptIDs assigned (20 per participant) | | postSurveyDifficulty | int | self-reported difficulty, 1–5 | | Age | int | years | | Gender, Race, Ethnicity | str | self-reported | | EnglishProficiency | str | Native speaker / Fluent / Conversational | | Country | str | always `United States` in this sample | | Education | str | self-reported, six levels | | TotalSessionDuration | float | total session, ms | | PassedAttention | bool | always True (failed participants are excluded) | | AvgTrialDuration | float | mean trial duration, ms | ### `recruitment_summary.csv` Counts per condition before exclusions: `Recruited`, `Completed`, and `PassedAttention` (text 53/49/48, audio 53/51/46). Used for Table 5. ### `long_audio_table_normalized.csv` / `long_text_table_normalized.csv` One row per (participant × rated response). The main analysis inputs. | column | type | description | |---|---|---| | PromptID | str | `_`, matches `prompt_stim_table` | | ParticipantID | str | `P001`..`P094` | | AudioClip | str | filename of the rated response (`.mp3`; also the response key in the text table) | | TrialNumber | int | 0 = attention check, 1–20 = real trials | | IsAttentionCheck | bool | | | PresentedFirst | bool | this response was shown first on the trial | | Rating | int | 1–100 response-quality rating | | qualityRating | int | audio only: 1–100 audio-quality rating | | overallPreference | str | the participant's A / Tie / B choice for the pair | | revisedAfterBothRated | bool | a rating was edited after both were set | | L | float | shrunk logit of Rating, `log(p / (1 − p))` with `p = (Rating + 0.5) / 101` | | L_adjusted | float | L with the condition's mean position bias (second-shown minus first-shown) removed from second-shown responses | | user_mean, user_sd | float | participant's mean and SD of L_adjusted | | L_centered | float | L_adjusted − user_mean | | z | float | L_centered / user_sd | | Pref_Num | float | 1 = this response preferred, 0.5 = tie, 0 = the other response | | Pref_Nom | int | the pair's choice: 1 = A, 2 = Tie, 3 = B | | z_binned | int | decile of z, used in descriptive plots | ### `long_audio_table_clean.csv` / `long_text_table_clean.csv` The same rows without the participant-normalization columns. ### `audio_data_table_clean.csv` / `text_data_table_clean.csv` One row per (participant × trial), with responses A and B side by side: both ratings and quality ratings, preference, justification, task duration, relisten counts (audio), and rating-change counts. ### `justifications_by_prompt_participant.csv` Free-text justifications with `prompt`, `participantID`, `modality`. ### `justifications_by_prompt_respondent.csv` The same justifications without participant or prompt keys (`PreferenceJustificationText`, `Modality`), as used by the TF-IDF analysis. ## Stimulus tables ### `prompt_stim_table.csv` One row per prompt pair: the user query, prior conversation history, and the two audio clip names. ### `survey_long_format.csv` One row per (PromptID × response): `AudioClip`, `AudioLength`, `TextResponse`, `TextLength`, and `PrismRating`, the original PRISM respondent's own rating. PrismRating is a human baseline, not an AI rating. ### `stimulus_topic_codes.csv`, `stimulus_topic_codes_raw.csv`, `stimulus_topics.json` Inductive topic coding of the 100 prompts (Table 19). - `stimulus_topics.json` is the induced topic list (11 topics), with the generation counts and merges that produced it. - `stimulus_topic_codes_raw.csv` has one row per (prompt × coder), with the supporting quote and a `context_dependent` flag. - `stimulus_topic_codes.csv` is the adjudicated code per prompt, joined with that prompt's cross-modal gap, winner flip, and modality-slope BLUP. The coders are two LLMs (Gemini 3.6 Flash and GPT-4o), so every field is model-generated. ## Synthetic (AI) ratings ### `synth_long_audio_table.csv` / `synth_long_text_table.csv` GPT-4o ratings of the same stimuli, in the same long schema as the human tables. Text: `gpt-4o-2024-11-20`; audio: `gpt-4o-audio-preview-2024-12-17`. These are the ratings used in the paper's Tables 3–4. `synth_long_*_table_gpt4o.csv` holds the same rows under the per-model name, plus a `model` column. ### `synth_long_{audio,text}_table_.csv` The same procedure with two more rater families (Tables 6–8): | suffix | text model | audio model (audio-native) | |---|---|---| | `_gemini_36_flash` | `gemini-3.6-flash` | `gemini-3.6-flash` | | `_qwen35_omni_flash` | `qwen3.5-flash` | `qwen3.5-omni-flash` | Each adds a `model` column. Every rater makes 10 independent generations per pair (`synthNo` 1–10), with presentation order counterbalanced 5/5. `PresentedFirst` records the order actually shown, and `Rating` is mapped back to the original A/B assignment. Audio models take the waveform directly, with no speech-to-text step. `qualityRating` in the audio tables is exploratory. Raters agree poorly on it (panel ICC(2,k) = 0.32, against 0.83 for the response rating), and each rater's quality score tracks its own content rating more than objective DistillMOS. ### `synthetic_ratings_10x.csv` / `synthetic_ratings_10x_.csv` The raw wide model output behind the `synth_long_*` tables (converted by `analysis/01_data_prep/03_synth_to_long_csv_multi.py`). One row per prompt with: - the PRISM conversation fields (`user_id`, `conv_id`, `turn_no`, `query`, both responses and lengths, audio filenames and durations); - ten rating blocks per modality: `text_rating_{a,b}_`, `text_justification_`, `text_order_a_first_`, and the audio equivalents, including `audio_quality_rating_{a,b}_`; - `mos_rating_{a,b}` from DistillMOS. `user_id` and `conv_id` are PRISM's own published identifiers, not study participants. The `*_justification_*` columns are model-generated text. ### `synthetic_ratings_fullcorpus_gemini_36_flash.csv` Gemini 3.6 Flash ratings of the **full** unguided PRISM corpus (10,800 prompt pairs), not just the 100 study pairs, in both modalities. It uses the same model, prompts, settings, and output schema as the study's Gemini ratings, but makes **one** generation per pair instead of ten, and it has no DistillMOS columns. Audio is the full-corpus Kokoro TTS (`stimuli/main_study/audio_full_zips/`); `audio_file_{1,2}` join to `full_audio_index.csv` on `audio_file`. One row per pair with: - the PRISM conversation fields as above, plus `unsum_hist` / `sum_hist` (unsummarized and summarized conversation history) and `prev_hist` (the history given to the model; empty for first turns); - one rating block per modality, in the same layout as the 10x files with `` = 1: `text_rating_{a,b}_1`, `text_justification_1`, `text_order_a_first_1`, `response_rating_{a,b}_1`, `audio_quality_rating_{a,b}_1`, `justification_1`, `audio_order_a_first_1`. Ratings are 1–100 and mapped back to the original A/B assignment; - `rater_model` (`gemini_36_flash`) and `row_id` (0–10,799, the pair's position in the corpus). Presentation order was drawn per pair and modality (seed 0) and is balanced (A first in 49.7% of text and 50.0% of audio pairs). These ratings are not used in the paper's analyses. ## Provenance The human tables were generated by `analysis/01_data_prep/02_convert_main_json_to_csv.py` from a Firestore export of the live study, then anonymized. The stimulus tables come from `analysis/01_data_prep/01_build_stim_tables.py`. Analysis outputs are in `results/`, not here.