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| # -*- coding: utf-8 -*- | |
| """Quantitative evidence preparation for SmartPaper's AMJ-oriented track.""" | |
| from __future__ import annotations | |
| import json | |
| import logging | |
| import re | |
| import numpy as np | |
| import pandas as pd | |
| from i18n import tr | |
| from utils.file_parser import extract_data_from_files | |
| class QuantitativeEngine: | |
| """Prepare one frozen quantitative evidence packet before manuscript writing.""" | |
| def generate_psd_corr_matrix(n_constructs, max_corr=0.5): | |
| if n_constructs == 1: | |
| return np.array([[1.0]]) | |
| base_corr = np.random.uniform(0.2, max_corr - 0.1) | |
| matrix = np.random.uniform( | |
| base_corr - 0.1, | |
| base_corr + 0.1, | |
| (n_constructs, n_constructs), | |
| ) | |
| matrix = (matrix + matrix.T) / 2 | |
| np.fill_diagonal(matrix, 1.0) | |
| min_eig = np.min(np.linalg.eigvals(matrix)) | |
| if min_eig < 0: | |
| matrix -= (min_eig - 0.01) * np.eye(n_constructs) | |
| diagonal = np.sqrt(np.diag(matrix)) | |
| return matrix / np.outer(diagonal, diagonal) | |
| def calculate_validity_metrics(df, constructs_info, true_loadings): | |
| metrics = [] | |
| for construct in constructs_info: | |
| construct_name = construct["name"] | |
| items = construct["items"] | |
| sub_df = df[items] | |
| var_total = sub_df.sum(axis=1).var(ddof=1) | |
| sum_var = sub_df.var(ddof=1).sum() | |
| n_items = len(items) | |
| alpha = ( | |
| 0.0 | |
| if var_total <= 0 | |
| else (n_items / (n_items - 1)) | |
| * (1 - sum_var / var_total) | |
| ) | |
| loadings = np.array(true_loadings[construct_name]) | |
| sum_loadings_sq = np.sum(loadings) ** 2 | |
| sum_sq_loadings = np.sum(loadings**2) | |
| sum_error = np.sum(1 - loadings**2) | |
| cr = sum_loadings_sq / (sum_loadings_sq + sum_error) | |
| ave = sum_sq_loadings / len(loadings) | |
| metrics.append( | |
| { | |
| "Construct": construct_name, | |
| "Items": n_items, | |
| "Cronbach's α": round(alpha, 3), | |
| "CR": round(cr, 3), | |
| "AVE": round(ave, 3), | |
| } | |
| ) | |
| return pd.DataFrame(metrics) | |
| def _safe_constructs(values): | |
| cleaned = [] | |
| for value in values or []: | |
| name = re.sub(r"\s+", " ", str(value or "")).strip(" .,:;-_\n\t") | |
| if not name or len(name) > 80 or name.lower() in {item.lower() for item in cleaned}: | |
| continue | |
| cleaned.append(name) | |
| return cleaned[:3] | |
| def _derive_system_design(self, agent, topic="", keywords=""): | |
| """Freeze a parsimonious topic-aligned design without inventing named sites.""" | |
| fallback = { | |
| "constructs": [ | |
| "Focal Predictor", | |
| "Focal Mediator", | |
| "Focal Outcome", | |
| ], | |
| "research_context": "respondents with experience relevant to the focal management phenomenon", | |
| } | |
| if agent is None: | |
| return fallback | |
| prompt = f"""Design the minimum coherent quantitative management-study skeleton for the topic below. | |
| Return ONLY valid JSON: | |
| {{"constructs":["predictor construct","mediating construct","outcome construct"],"research_context":"one generic respondent context"}} | |
| Rules: | |
| 1. Exactly three distinct constructs in predictor -> mediator -> outcome order. | |
| 2. Use concise construct labels that can appear throughout a journal article. | |
| 3. The context must be generic and directly relevant to the topic. Do NOT name or invent a country, university, firm, industry sample frame, software package, time period, or institution. | |
| 4. This is a cross-sectional single-wave survey design, so do not imply experimental or causal identification. | |
| 5. Do not create any number; sample size and scale are frozen elsewhere. | |
| Topic: {topic} | |
| Keywords: {keywords}""" | |
| try: | |
| raw = agent.call_deepseek( | |
| "You are a rigorous management research-design editor.", | |
| prompt, | |
| max_tokens=500, | |
| temp=0.0, | |
| model_role="fast", | |
| thinking=False, | |
| allow_fallback=True, | |
| ) | |
| parsed = agent._extract_json_from_text(raw) | |
| if not isinstance(parsed, dict): | |
| return fallback | |
| constructs = self._safe_constructs(parsed.get("constructs")) | |
| context = re.sub(r"\s+", " ", str(parsed.get("research_context") or "")).strip() | |
| if len(constructs) != 3: | |
| return fallback | |
| if not context or len(context) > 240: | |
| context = fallback["research_context"] | |
| # Named-location/site details are deliberately not accepted from this | |
| # design step. The manuscript may remain generic when no user evidence | |
| # supplies a real setting. | |
| if re.search(r"\b(university|company|corporation|inc\.?|ltd\.?|country|china|usa|united states|europe|asia)\b", context, re.I): | |
| context = fallback["research_context"] | |
| return {"constructs": constructs, "research_context": context} | |
| except Exception as exc: | |
| logging.warning("Quantitative study-design freeze fell back safely: %s", exc) | |
| return fallback | |
| def generate_empirical_data_robust( | |
| self, | |
| construct_names=None, | |
| n=300, | |
| scale_points=7, | |
| ): | |
| if not construct_names or len(construct_names) < 2: | |
| construct_names = [ | |
| "Focal Predictor", | |
| "Focal Mediator", | |
| "Focal Outcome", | |
| ] | |
| n_constructs = len(construct_names) | |
| corr = self.generate_psd_corr_matrix(n_constructs, max_corr=0.55) | |
| latent_scores = np.random.multivariate_normal( | |
| mean=[0] * n_constructs, | |
| cov=corr, | |
| size=n, | |
| ) | |
| data = {} | |
| true_loadings = {} | |
| constructs_info = [] | |
| target_mean = scale_points / 2.0 + 0.5 | |
| target_std = scale_points / 5.0 | |
| for index, construct_name in enumerate(construct_names): | |
| true_score = latent_scores[:, index] | |
| n_items = np.random.randint(3, 6) | |
| loadings = np.random.uniform(0.72, 0.88, n_items) | |
| true_loadings[construct_name] = loadings | |
| item_names = [] | |
| safe_prefix = re.sub(r"[^A-Za-z0-9]+", "_", construct_name).strip("_") or f"Construct_{index + 1}" | |
| for item_index, loading in enumerate(loadings): | |
| error = np.random.normal( | |
| 0, | |
| np.sqrt(1 - loading**2), | |
| n, | |
| ) | |
| item_score = loading * true_score + error | |
| raw_scaled = item_score * target_std + target_mean | |
| item_int = np.clip( | |
| np.round(raw_scaled), | |
| 1, | |
| scale_points, | |
| ).astype(int) | |
| column_name = f"{safe_prefix}_{item_index + 1}" | |
| data[column_name] = item_int | |
| item_names.append(column_name) | |
| constructs_info.append( | |
| {"name": construct_name, "items": item_names} | |
| ) | |
| df = pd.DataFrame(data) | |
| validity_df = self.calculate_validity_metrics( | |
| df, | |
| constructs_info, | |
| true_loadings, | |
| ) | |
| mean_scores = pd.DataFrame( | |
| { | |
| construct["name"]: df[construct["items"]].mean(axis=1) | |
| for construct in constructs_info | |
| } | |
| ) | |
| corr_matrix = mean_scores.corr() | |
| fl_matrix = corr_matrix.astype(object) | |
| for _, row in validity_df.iterrows(): | |
| construct_name = row["Construct"] | |
| sqrt_ave = np.sqrt(row["AVE"]) | |
| fl_matrix.loc[construct_name, construct_name] = ( | |
| f"({sqrt_ave:.3f})" | |
| ) | |
| for row_index in range(len(fl_matrix.columns)): | |
| for column_index in range(row_index + 1, len(fl_matrix.columns)): | |
| fl_matrix.iloc[row_index, column_index] = "" | |
| for column_index in range(row_index): | |
| value = fl_matrix.iloc[row_index, column_index] | |
| if isinstance(value, (int, float, np.integer, np.floating)): | |
| fl_matrix.iloc[row_index, column_index] = f"{float(value):.3f}" | |
| desc_stats = mean_scores.mean().to_frame("Mean").join( | |
| mean_scores.std().to_frame("SD") | |
| ) | |
| return df, validity_df, desc_stats.join(fl_matrix), mean_scores, corr_matrix | |
| def _standardized_ols(y, predictors): | |
| y = np.asarray(y, dtype=float) | |
| x = np.column_stack([np.asarray(value, dtype=float) for value in predictors]) | |
| y_sd = y.std(ddof=1) | |
| x_sd = x.std(axis=0, ddof=1) | |
| if y_sd <= 0 or np.any(x_sd <= 0): | |
| raise RuntimeError("quantitative_zero_variance") | |
| yz = (y - y.mean()) / y_sd | |
| xz = (x - x.mean(axis=0)) / x_sd | |
| design = np.column_stack([np.ones(len(yz)), xz]) | |
| coef = np.linalg.lstsq(design, yz, rcond=None)[0] | |
| fitted = design @ coef | |
| ss_res = float(np.sum((yz - fitted) ** 2)) | |
| ss_tot = float(np.sum((yz - yz.mean()) ** 2)) | |
| r2 = 0.0 if ss_tot <= 0 else 1.0 - ss_res / ss_tot | |
| return coef[1:], max(0.0, min(1.0, r2)) | |
| def _path_evidence(self, mean_scores, construct_names, bootstrap_iterations=1500): | |
| if len(construct_names) < 3: | |
| return {}, pd.DataFrame(), pd.DataFrame() | |
| x_name, m_name, y_name = construct_names[0], construct_names[1], construct_names[2] | |
| x = mean_scores[x_name].to_numpy(dtype=float) | |
| m = mean_scores[m_name].to_numpy(dtype=float) | |
| y = mean_scores[y_name].to_numpy(dtype=float) | |
| a_coef, r2_m = self._standardized_ols(m, [x]) | |
| y_coef, r2_y = self._standardized_ols(y, [x, m]) | |
| a = float(a_coef[0]) | |
| direct = float(y_coef[0]) | |
| b = float(y_coef[1]) | |
| indirect = a * b | |
| rng = np.random.default_rng(20260809) | |
| indirect_boot = [] | |
| direct_boot = [] | |
| n = len(mean_scores) | |
| for _ in range(int(bootstrap_iterations)): | |
| idx = rng.integers(0, n, n) | |
| try: | |
| a_b, _ = self._standardized_ols(m[idx], [x[idx]]) | |
| y_b, _ = self._standardized_ols(y[idx], [x[idx], m[idx]]) | |
| indirect_boot.append(float(a_b[0] * y_b[1])) | |
| direct_boot.append(float(y_b[0])) | |
| except Exception: | |
| continue | |
| if len(indirect_boot) < 100: | |
| raise RuntimeError("quantitative_bootstrap_insufficient") | |
| indirect_ci = np.percentile(indirect_boot, [2.5, 97.5]) | |
| direct_ci = np.percentile(direct_boot, [2.5, 97.5]) | |
| path_table = pd.DataFrame( | |
| [ | |
| {"Association": f"{x_name} → {m_name}", "Standardized estimate": round(a, 3), "R² of outcome": round(r2_m, 3)}, | |
| {"Association": f"{m_name} → {y_name} (controlling {x_name})", "Standardized estimate": round(b, 3), "R² of outcome": round(r2_y, 3)}, | |
| {"Association": f"{x_name} → {y_name} (controlling {m_name})", "Standardized estimate": round(direct, 3), "R² of outcome": round(r2_y, 3)}, | |
| {"Association": f"Indirect association: {x_name} → {m_name} → {y_name}", "Standardized estimate": round(indirect, 3), "R² of outcome": "—"}, | |
| ] | |
| ) | |
| bootstrap_table = pd.DataFrame( | |
| [ | |
| {"Estimate": "Indirect association", "Point estimate": round(indirect, 3), "95% bootstrap CI lower": round(float(indirect_ci[0]), 3), "95% bootstrap CI upper": round(float(indirect_ci[1]), 3)}, | |
| {"Estimate": "Residual direct association", "Point estimate": round(direct, 3), "95% bootstrap CI lower": round(float(direct_ci[0]), 3), "95% bootstrap CI upper": round(float(direct_ci[1]), 3)}, | |
| ] | |
| ) | |
| x_m_corr = float(np.corrcoef(x, m)[0, 1]) | |
| vif = 1.0 / max(1e-9, 1.0 - x_m_corr**2) | |
| spearman = mean_scores.corr(method="spearman") | |
| summary = { | |
| "predictor": x_name, | |
| "mediator": m_name, | |
| "outcome": y_name, | |
| "a": round(a, 3), | |
| "b": round(b, 3), | |
| "direct": round(direct, 3), | |
| "indirect": round(indirect, 3), | |
| "r2_mediator": round(r2_m, 3), | |
| "r2_outcome": round(r2_y, 3), | |
| "indirect_ci": [round(float(indirect_ci[0]), 3), round(float(indirect_ci[1]), 3)], | |
| "direct_ci": [round(float(direct_ci[0]), 3), round(float(direct_ci[1]), 3)], | |
| "predictor_mediator_vif": round(float(vif), 3), | |
| "bootstrap_iterations": int(bootstrap_iterations), | |
| } | |
| return summary, path_table, spearman | |
| def resolve_empirical_data( | |
| self, | |
| uploaded_files=None, | |
| language="zh", | |
| *, | |
| topic="", | |
| keywords="", | |
| agent=None, | |
| ): | |
| if uploaded_files: | |
| user_md = extract_data_from_files(uploaded_files) | |
| if user_md: | |
| evidence_sections = [ | |
| heading.strip() | |
| for heading in re.findall( | |
| r"(?m)^#{1,3}\s+(.+?)\s*$", | |
| user_md, | |
| ) | |
| if heading.strip() | |
| ] | |
| metadata = { | |
| "mode": "quantitative", | |
| "source_type": "user_provided", | |
| "disclosure_required": False, | |
| "evidence_sections": evidence_sections[:20] | |
| or ["User-provided quantitative evidence"], | |
| "evidence_boundary": ( | |
| "Only values explicitly present in the uploaded evidence are frozen. " | |
| "Do not infer absent setting, sample, measures, software, or results." | |
| ), | |
| } | |
| return None, user_md, tr(language, "actual_stats_parsed"), metadata | |
| design = self._derive_system_design(agent, topic, keywords) | |
| construct_names = design["constructs"] | |
| df, validity_df, fl_matrix_df, mean_scores, corr_matrix = self.generate_empirical_data_robust( | |
| construct_names=construct_names | |
| ) | |
| path_summary, path_table, spearman = self._path_evidence(mean_scores, construct_names) | |
| context = design["research_context"] | |
| generated_prompt = f"""### Frozen Quantitative Study Design | |
| | Field | Frozen value | | |
| | :--- | :--- | | |
| | Design | Cross-sectional, single-wave questionnaire study | | |
| | Respondent context | {context} | | |
| | Sample size | {len(df)} | | |
| | Response scale | 7-point Likert-type scale | | |
| | Predictor | {construct_names[0]} | | |
| | Mediator | {construct_names[1]} | | |
| | Outcome | {construct_names[2]} | | |
| | Causal identification | None; report associations and estimated indirect association, not causal effects | | |
| ### Table 1: Reliability and Convergent Validity | |
| {validity_df.to_markdown(index=False)} | |
| ### Table 2: Descriptive Statistics and Discriminant Validity (Fornell-Larcker Criterion) | |
| *Note: Diagonal elements in parentheses are the square roots of AVE.* | |
| {fl_matrix_df.to_markdown()} | |
| ### Table 3: Frozen Association Model Results | |
| {path_table.to_markdown(index=False)} | |
| ### Table 4: Rank-Order Robustness Correlations (Spearman) | |
| {spearman.round(3).to_markdown()} | |
| ### Frozen Bootstrap and Collinearity Checks | |
| - Bootstrap resamples: {path_summary['bootstrap_iterations']}. | |
| - Indirect association ({construct_names[0]} → {construct_names[1]} → {construct_names[2]}): {path_summary['indirect']}; 95% bootstrap CI [{path_summary['indirect_ci'][0]}, {path_summary['indirect_ci'][1]}]. | |
| - Residual direct association ({construct_names[0]} → {construct_names[2]} controlling {construct_names[1]}): {path_summary['direct']}; 95% bootstrap CI [{path_summary['direct_ci'][0]}, {path_summary['direct_ci'][1]}]. | |
| - Predictor/mediator VIF in the outcome model: {path_summary['predictor_mediator_vif']}. | |
| [STRICT EMPIRICAL INSTRUCTION] | |
| 1. ZERO HALLUCINATION: Every study-specific fact and number must come from this frozen packet. Never invent a country, named organization, sampling frame, response rate, demographic characteristic, measure source, software package, coefficient, p-value, fit index, robustness test, or date. | |
| 2. DESIGN DISCIPLINE: This is a cross-sectional observational survey. Use association language. The indirect estimate is not causal mediation identification. | |
| 3. MEASUREMENT DEFENSE: State reliability/convergent/discriminant validity only when the displayed values support it. | |
| 4. RESULTS DISCIPLINE: Results must report this study's frozen numbers without attaching external citations to the coefficients or sample facts. | |
| 5. ROBUSTNESS: The Spearman matrix, bootstrap intervals, and VIF above are the only frozen robustness evidence. Do not add another robustness procedure. | |
| 6. CONSISTENCY: The predictor, mediator, outcome, sample size, context, scale, and all numerical results above are the single source of quantitative truth throughout the manuscript. | |
| """ | |
| metadata = { | |
| "mode": "quantitative", | |
| "source_type": "system_generated", | |
| "disclosure_required": True, | |
| "sample_size": int(len(df)), | |
| "scale_points": 7, | |
| "study_design": "cross-sectional single-wave questionnaire", | |
| "research_context": context, | |
| "constructs": { | |
| "predictor": construct_names[0], | |
| "mediator": construct_names[1], | |
| "outcome": construct_names[2], | |
| }, | |
| "association_results": path_summary, | |
| "method": ( | |
| "Positive-semidefinite latent correlation generation, indicator loadings, " | |
| "Likert discretization, reliability/validity summaries, standardized association " | |
| "models, fixed-seed bootstrap intervals, and Spearman robustness correlations." | |
| ), | |
| "generation_parameters": { | |
| "constructs": construct_names, | |
| "sample_size": int(len(df)), | |
| "likert_scale_points": 7, | |
| "items_per_construct": "random integer from 3 to 5", | |
| "indicator_loading_range": [0.72, 0.88], | |
| "latent_correlation_control": "positive-semidefinite matrix with maximum target correlation 0.55", | |
| "item_generation": "loading × latent score + normally distributed measurement error", | |
| "discretization": "round to nearest integer and clamp to 1–7", | |
| "bootstrap_iterations": path_summary["bootstrap_iterations"], | |
| "bootstrap_seed": 20260809, | |
| "reported_summaries": [ | |
| "Cronbach alpha", | |
| "composite reliability", | |
| "average variance extracted", | |
| "descriptive statistics", | |
| "Fornell-Larcker matrix", | |
| "standardized association estimates", | |
| "R-squared", | |
| "bootstrap confidence intervals", | |
| "Spearman correlations", | |
| "VIF", | |
| ], | |
| }, | |
| "evidence_sections": [ | |
| "Frozen quantitative study design", | |
| "Reliability and convergent validity", | |
| "Descriptive statistics and discriminant validity", | |
| "Frozen association model results", | |
| "Rank-order robustness correlations", | |
| "Frozen bootstrap and collinearity checks", | |
| ], | |
| } | |
| return ( | |
| df, | |
| generated_prompt, | |
| tr(language, "synthetic_stats_generated"), | |
| metadata, | |
| ) | |
| def get_quantitative_engine(): | |
| return QuantitativeEngine() | |