Bibliographic record
Abstract
This deliverable provides the data-collection blueprint for the project “Why People Do Not Use Financial Planning.” It lays out a transparent, replicable protocol for building a multi-platform corpus of real-world consumer narratives and complaints, focusing on barriers faced by low- and middle-income households in the U.S. and Canada. The primary corpus is drawn from four major public platforms—Google (search and reviews), Reddit, X (Twitter), and Quora—so that we can capture intent, lived experience, real-time discourse, and explicitly stated reasons for (not) using financial planners. Supplementary benchmarking sources include CFPB’s Consumer Complaint Database, Better Business Bureau records, and Yelp reviews, which are used to cross-validate and enrich the barrier taxonomy. The protocol specifies search strategies, platform-specific filters, time windows, and metadata to retain (e.g., timestamps, engagement metrics, geography) as well as governance rules such as compliance with robots.txt/Terms of Service, exclusion of paywalled or login-gated content, and procedures for anonymization and PII scrubbing. It also documents known limitations—sampling bias, short/noisy texts, astroturfing/fraud—and the mitigation steps built into the pipeline (post-stratification, topic-model stability checks, spam/fake-review screening). Together, this deliverable serves as a methodological foundation for the project’s subsequent topic-modeling and sentiment/framing analyses and as a reusable template for other researchers who wish to apply NLP to public web data in consumer-finance contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.597 | 0.424 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".