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Exclusion criteria updated in README.md

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  1. README.md +7 -5
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@@ -51,7 +51,7 @@ Below is the detailed table of metrics with their definitions and scales:
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  | **Complexity** | The explanation has an appropriate level of detail and complexity - not too simple, yet not overly complex. | from -2 (too simple) to 0 (ideal complexity) to 2 (too complex) |
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  ## Evaluators
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- The evaluators were selected from the Prolific platform, ensuring a diverse demographic with varying levels of expertise. The selection was based on the criteria of being at least 18 years old, fluent in English. Here’s an overview of the evaluator demographics and their distribution:
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  | Demographic | Description | Percentage (%) |
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  |------------------------------|------------------------------------------------------|----------------|
@@ -101,12 +101,14 @@ The evaluators were selected from the Prolific platform, ensuring a diverse demo
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  ### Exclusion criteria
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  The responses from participants were excluded if they failed any of the following quality checks:
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- - Attention Check: Hidden questions designed to flag participants who provided inconsistent or invalid responses.
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- - Response Time: Unusually fast responses indicating the participant may not have carefully read or evaluated the scenarios.
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- - Average Understandability Time: Unusually fast responses indicating the participant may not have carefully read or evaluated the scenarios.
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- - Indicator questions answerd: For example, marking infeasible scenarios (e.g., "change place of birth") as feasible.
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  - Uniform Response Patterns: Participants providing uniform or highly unvaried ratings across all metrics.
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  ## File Descriptions
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  - `cleaned_cf_dataset.csv`: This file contains the cleaned version of evaluator responses after applying exclusion criteria. Each row corresponds to a Scenario-Metric pair (30 scenarios × 8 metrics = 240 rows). Each column corresponds to a participant's ranking of the given Scenario-Metric pair.
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  - `full_cf_dataset.csv`: This file contains all evaluator responses before applying exclusion criteria.
 
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  | **Complexity** | The explanation has an appropriate level of detail and complexity - not too simple, yet not overly complex. | from -2 (too simple) to 0 (ideal complexity) to 2 (too complex) |
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  ## Evaluators
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+ The evaluators were recruited through the Prolific platform, with an average survey completion time of **42 minutes**. The selection was based on the criteria of being at least 18 years old, fluent in English. Here’s an overview of the evaluator demographics and their distribution:
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  | Demographic | Description | Percentage (%) |
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  |------------------------------|------------------------------------------------------|----------------|
 
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  ### Exclusion criteria
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  The responses from participants were excluded if they failed any of the following quality checks:
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+ - Attention Check: Participants were required to write specific text in a field, differing from the usual question format, to ensure attentiveness.
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+ - Response Time: Participants who completed the survey significantly faster than the expected minimum time were flagged.
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+ - Average Understandability Time: Participants with consistently low understandability ratings were flagged.
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+ - Indicator questions answerd: Specific questions with known extreme values for certain criteria were included to verify if participants' answers aligned with the expected evaluation. For example, marking infeasible scenarios (e.g., "change place of birth") as feasible.
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  - Uniform Response Patterns: Participants providing uniform or highly unvaried ratings across all metrics.
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+ IDs of excluded participants: 12, 27, 28, 44, 75, 83, 84, 88, 97, 201.
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+
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  ## File Descriptions
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  - `cleaned_cf_dataset.csv`: This file contains the cleaned version of evaluator responses after applying exclusion criteria. Each row corresponds to a Scenario-Metric pair (30 scenarios × 8 metrics = 240 rows). Each column corresponds to a participant's ranking of the given Scenario-Metric pair.
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  - `full_cf_dataset.csv`: This file contains all evaluator responses before applying exclusion criteria.