Sit Back and Enjoy The Ride: Financial planners and the symbolic domination of clients.
Bibliographic record
Abstract
Borrowing from Bourdieu’s theory of practice, specifically, the relationship between forms of capital and discourse on the one hand and the nature of symbolic domination on the other (see Bourdieu 1998; 1991), this paper seeks to answer the following question: what discursive strategies do personal financial planners use to facilitate desirable client behavior vis-à-vis market investment? On the basis of 32 semi-structured interviews with financial planners and textual analyses of relevant industry materials, I argue that planners use three essential discursive strategies: the naturalization of market volatility, the establishing of reasonable expectations, and the managing of external discourses. Together, these discursive strategies facilitate the symbolic domination of clients while cultivating a professional relationship amenable to long term investment and profitability.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.048 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".