Bibliographic record
Abstract
This second deliverable builds on the web-data protocol (Deliverable 1) and presents the substantive findings from topic modeling and sentiment analysis of consumer narratives about financial planning. Using a cleaned corpus of short, real-world comments from multiple online platforms, the report analyzes both a broad sample of public voices and focused subsets that highlight (1) people who explicitly say they do not use financial planners or advisors, (2) individuals who mention low income, poverty, or financial hardship, and (3) Canadian consumers, including those referencing products such as RRSPs, TFSAs, and CPP/QPP. Through LDA topic modeling and qualitative interpretation, the report surfaces recurring themes such as: “It’s too expensive,” “I don’t trust the system or the advisors,” “I can manage on my own,” and “I feel ashamed or judged.” A second thematic map zooms in on low-income and financially vulnerable groups, showing how many perceive planning as something “for rich people,” feel that advice does not fit their unstable reality, or experience practical access barriers. Each theme is followed by practice-oriented implications for planners and organizations (e.g., fee transparency, scaled service models, non-judgmental communication, partnerships with community organizations). The document is structured in three parts—an executive summary, a practitioner-focused comprehensive report, and a detailed analytic/technical appendix—so that researchers, policymakers, and practitioners can all use the findings at the level of depth they need. This project relies exclusively on publicly available big data collected from open web sources via web crawling. Because no identifiable human subjects data are included, this work did not require IRB review.
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.598 | 0.380 |
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".