pipeline(validate-ideas): 1h tick

Author: github-actions[bot]

projects/PROJ-034-quantifying-uncertainty-in-small-sample-/.specify/memory/research_question_validated.yaml

@@ -0,0 +1,2 @@
+validated: true
+validated_at: 2026-06-12T02:40:07.386541+00:00

projects/PROJ-034-quantifying-uncertainty-in-small-sample-/idea/research_question_validation.md

@@ -0,0 +1,31 @@
+## Research-question validation
+
+### Phenomenon-vs-method check
+
+**Verdict**: pass
+
+The question asks about the statistical properties of two uncertainty quantification procedures (Bayesian vs. bootstrap) under well-defined data conditions (small N, collinearity). This is a legitimate methodological research question in statistics where the "phenomenon" is the behavior of estimators under finite-sample constraints, not a specific implementation constraint.
+
+### Circularity check
+
+**Verdict**: pass
+
+The predictor (choice of uncertainty quantification method) and the predicted variable (empirical coverage probability) are independent. Coverage probability is measured by whether the true parameter falls within constructed intervals across Monte Carlo replications, not mechanically determined by the method choice itself.
+
+### Triviality check
+
+**Verdict**: pass
+
+Either outcome is informative: if Bayesian methods maintain better coverage, this validates weakly informative priors for small-sample inference; if bootstrap performs comparably, this would support bootstrap as a computationally simpler alternative. Both results would contribute to the comparative literature gap identified in the motivation.
+
+### Question-narrowing check
+
+**Verdict**: pass
+
+Names a domain relationship (coverage probability as a function of method choice under specific data conditions: N < 50, collinearity). The implementation details (PyMC3, 1000 resamples, 4-hour runtime) are in the methodology section, not the research question itself.
+
+### Overall verdict
+
+**Verdict**: validated
+
+All four checks pass. This is a legitimate methodological research question in statistics asking about the empirical properties of uncertainty quantification procedures under finite-sample constraints. The question is independent of specific implementation constraints and both positive and null results would be informative to the statistical community.

projects/PROJ-229-investigating-the-predictive-power-of-ma/idea/research_question_validation.md

@@ -0,0 +1,34 @@
+## Research-question validation
+
+### Phenomenon-vs-method check
+
+**Verdict**: fail
+
+The question is framed as "Can ML models... accurately predict" and "How does model performance vary" — both are method-evaluation questions rather than questions about what physical or compositional factors determine phase-change suitability. The phenomenon question buried underneath would be "Which material properties and structural features make a compound suitable for phase-change applications?"
+
+### Circularity check
+
+**Verdict**: pass
+
+The predictors (elemental descriptors, crystal structure graphs) are derived from material composition and crystallographic structure, while the predicted variables (melting point, latent heat, specific heat capacity) are thermodynamic measurements. These are independent properties measured or calculated separately in the Materials Project and NIST databases, not derived from the same primary signal.
+
+### Triviality check
+
+**Verdict**: concern
+
+ML property prediction in materials science is already well-established for many properties (as evidenced by the CGCNN and related work cited). A positive result (ML works for PCM properties) would confirm existing expectations. A null result could be informative if it suggests PCM properties depend on factors not captured by composition/structure alone, but the question as framed doesn't isolate what would make a null result scientifically meaningful.
+
+### Question-narrowing check
+
+**Verdict**: fail
+
+The question names implementation constraints and method comparisons ("ML models," "feature representations," "model performance") rather than a domain relationship. "Can ML predict X" is a benchmark question; "What features of X determine Y" would be a domain question.
+
+### Overall verdict
+
+**Verdict**: validator_revise
+
+[REVISED]
+Which compositional and structural features (elemental properties, bonding patterns, crystal symmetry) most strongly determine phase-change material suitability, and how can interpretable ML models identify these governing factors beyond black-box prediction accuracy?
+[/REVISED]
+The reframing shifts from "can ML work" to "what determines PCM suitability," making the ML model a tool for scientific discovery rather than the subject of the question itself. This preserves the methodology while making the research question scientifically substantive.

projects/PROJ-249-the-impact-of-digital-decluttering-on-co/.specify/memory/research_question_validated.yaml

@@ -0,0 +1,2 @@
+validated: true
+validated_at: 2026-06-12T02:38:34.748439+00:00

projects/PROJ-249-the-impact-of-digital-decluttering-on-co/idea/research_question_validation.md

@@ -0,0 +1,31 @@
+## Research-question validation
+
+### Phenomenon-vs-method check
+
+**Verdict**: pass
+
+The question asks about a substantive relationship between digital engagement patterns and human cognitive/emotional functioning. It is framed as a causal inquiry about an intervention's effect on measurable outcomes, independent of any specific computational method's performance.
+
+### Circularity check
+
+**Verdict**: pass
+
+The predictor (self-reported digital engagement levels before and after intervention) and predicted variables (cognitive task performance from SART/Ospan, stress/mood from PSS-10/PANAS) come from independent measurement sources. Digital engagement is the intervention, while cognitive and emotional outcomes are assessed separately through behavioral tasks and validated questionnaires.
+
+### Triviality check
+
+**Verdict**: pass
+
+A positive result would support Attention Restoration Theory and provide evidence for a low-cost cognitive intervention; a null result would challenge assumptions about short-term disengagement benefits and suggest either insufficient intervention duration or more stable cognitive baselines. Both outcomes would be informative to the literature.
+
+### Question-narrowing check
+
+**Verdict**: pass
+
+Names a clear domain relationship (digital engagement → cognitive performance and well-being) rather than implementation constraints. The question is about human behavior and cognition, not about whether a specific algorithm can handle a task within resource limits.
+
+### Overall verdict
+
+**Verdict**: validated
+
+All four checks pass. The research question is well-framed as a substantive inquiry into the relationship between digital behavior modification and cognitive/emotional outcomes. The question is independent of method-specific constraints, avoids circularity through independent measurements, and would yield informative results regardless of outcome. The project can proceed to initialization.

projects/PROJ-283-statistical-analysis-of-publicly-availab/.specify/memory/research_question_validated.yaml

@@ -0,0 +1,2 @@
+validated: true
+validated_at: 2026-06-12T02:40:41.420911+00:00

projects/PROJ-283-statistical-analysis-of-publicly-availab/idea/research_question_validation.md

@@ -0,0 +1,31 @@
+## Research-question validation
+
+### Phenomenon-vs-method check
+
+**Verdict**: pass
+
+The question asks about a substantive relationship between observable game features and Elo rating system performance, independent of any specific ML method. The regression models are tools to answer the question, not the subject of the question itself.
+
+### Circularity check
+
+**Verdict**: pass
+
+The predictors (opening codes, move times, material imbalance) are independent measurements from game PGN files, while the predicted variable (outcome deviation) combines actual game results with Elo expectations derived from player ratings. These are distinct data sources; the Elo expectation is a function of historical ratings, not the specific game features being tested.
+
+### Triviality check
+
+**Verdict**: pass
+
+Either outcome is informative: a positive result identifies systematic biases in Elo and informs more accurate rating models; a null result validates the Elo system's robustness despite its simplicity. Both would be publishable as they address whether the current rating standard captures all relevant predictive signal.
+
+### Question-narrowing check
+
+**Verdict**: pass
+
+The question names a domain relationship (game features → Elo prediction accuracy) rather than implementation constraints. Budget and runtime limits appear only in the methodology section, not in the research question itself.
+
+### Overall verdict
+
+**Verdict**: validated
+
+All four checks pass. The research question addresses a substantive phenomenon about chess rating systems, uses independent data sources, would produce publishable results regardless of outcome, and does not conflate implementation constraints with scientific inquiry.

projects/PROJ-385-the-impact-of-text-message-tone-on-perce/idea/research_question_validation.md

@@ -0,0 +1,34 @@
+## Research-question validation
+
+### Phenomenon-vs-method check
+
+**Verdict**: pass
+
+The question asks about a psychological relationship between message tone and perceived emotional support, independent of any specific measurement method or machine learning technique. This is a domain-level phenomenon question.
+
+### Circularity check
+
+**Verdict**: pass
+
+The predictor (text message tone, measured via linguistic/paralinguistic features) and the predicted variable (recipient's perception of emotional support, measured via survey/rating) are independent measurement sources. One is the stimulus, one is the response.
+
+### Triviality check
+
+**Verdict**: concern
+
+The relationship between communication tone and perceived support is well-established in communication psychology literature. A positive result ("tone affects support perception") would confirm existing theory rather than extend it. A null result could be informative (suggesting certain tones are universally interpreted), but may also indicate measurement issues. The question needs more specificity about what aspect of tone or which population makes this novel.
+
+### Question-narrowing check
+
+**Verdict**: pass
+
+Names a domain relationship (tone → perceived support) rather than an implementation constraint. No budget, method, or hardware constraints are embedded in the question.
+
+### Overall verdict
+
+**Verdict**: validator_revise
+
+[REVISED]
+How do specific paralinguistic features (emoji use, punctuation patterns, message length) in text messages from close ties versus acquaintances differentially predict perceived emotional support among adults aged 18-35?
+[/REVISED]
+This reframing adds novelty by specifying (1) which tone features are measured, (2) the relationship context (close ties vs. acquaintances), and (3) a specific demographic. Either finding—feature importance differences or tie-strength effects—would be publishable contributions to digital communication psychology.

projects/PROJ-412-predicting-molecular-surface-area-from-g/.specify/memory/research_question_validated.yaml

@@ -0,0 +1,2 @@
+validated: true
+validated_at: 2026-06-12T02:39:06.208626+00:00

projects/PROJ-412-predicting-molecular-surface-area-from-g/idea/research_question_validation.md

@@ -0,0 +1,31 @@
+## Research-question validation
+
+### Phenomenon-vs-method check
+
+**Verdict**: pass
+
+The question asks about the information content of 2D topology regarding 3D geometry, using the GCN merely as a tool to measure this relationship. It does not frame the inquiry around the performance of a specific architecture or resource constraint.
+
+### Circularity check
+
+**Verdict**: pass
+
+The predictor (2D bond connectivity) and the predicted variable (3D spatial surface area) are distinct representations of the molecule. Surface area depends on 3D conformation which is not fully specified by 2D topology, preventing a mechanical guarantee of the relationship.
+
+### Triviality check
+
+**Verdict**: pass
+
+A positive result would justify bypassing expensive 3D sampling in drug discovery pipelines, while a null result would confirm the necessity of conformational analysis. Both outcomes provide actionable insights for computational chemistry workflows.
+
+### Question-narrowing check
+
+**Verdict**: pass
+
+The question names a fundamental domain relationship (topology vs. geometry) rather than a constraint on the implementation. It asks about the extent of predictability in the physical system, not the feasibility of the model training.
+
+### Overall verdict
+
+**Verdict**: validated
+
+All four checks pass, confirming the research question is scientifically substantive and independent of methodological artifacts. The project can proceed to initialization without requiring reframing.