Use trustworthy terminology consistently in dataset card

README.md

@@ -32,13 +32,13 @@ The dataset supports training and evaluating code-review judges, including agent
 
 ## Task and labels
 
-The current project rubric is **technical correctness and correct file location**:
+The task is to determine whether the target code-review comment is **technically trustworthy** in its pull-request and file context:
 
-- **`true`**: the target comment is technically correct and attached to the correct file.
-- **`false`**: the target comment is technically incorrect or attached to the wrong file, even if its statement would hold in another file.
-- General PR comments without a file association are judged by technical correctness.
+- **`true`**: the target comment is technically trustworthy in the given context.
+- **`false`**: the target comment is technically untrustworthy in the given context.
+- General PR comments without a file association are assessed for technical trustworthiness in the overall PR context.
 
-A useful observation does not override a technical error. Missing evidence alone is not proof that a comment is wrong. The label evaluates an individual comment, not the trustworthiness of its author or the overall quality of the PR. All released labels are JSON booleans; none are missing.
+The `trustworthy` label applies to the individual target comment. All released labels are JSON booleans; none are missing.
 
 ## Dataset splits
 
@@ -115,11 +115,11 @@ gold_label = row["trustworthy"]  # For supervision or scoring only.
 4. For repository inspection, use the recorded `base_commit` and distinguish base-version evidence from evidence obtained after applying `patch_to_review`. Repository checkouts are not bundled with these files.
 5. Use training examples for parameter updates and teacher-generated supervision, validation for model selection, and the fixed test set for final evaluation. Preserve PR groups in any derived data.
 
-Treat `true` as the positive class. Report accuracy, macro-F1, per-class precision and recall, and invalid/missing prediction counts. Source-specific results help distinguish performance on natural incorrect comments from performance on perturbations; include sample counts for each slice.
+Treat `true` as the positive class. Report accuracy, macro-F1, per-class precision and recall, and invalid/missing prediction counts. Source-specific results help distinguish performance on naturally occurring untrustworthy comments from performance on perturbations; include sample counts for each slice.
 
 ## Limitations
 
-- Coverage is concentrated in a small number of repositories and in Python. The validation split contains only 8 PRs, only Python examples, and only 6 natural incorrect comments.
+- Coverage is concentrated in a small number of repositories and in Python. The validation split contains only 8 PRs, only Python examples, and only 6 naturally occurring untrustworthy comments.
 - Perturbations account for 370 of 435 negative examples. Performance may reflect sensitivity to these edits and may not generalize to the full range of naturally occurring review errors.
 - Multiple examples share PR context, and original comments can have perturbed counterparts. Examples are therefore correlated; PR-level grouping matters for both splitting and uncertainty estimates.
 - Labels are task-specific judgments. The release does not establish inter-annotator agreement or guarantee error-free annotations.