High-Dimensional Perception with the Double Machine Learning Lens Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Traditional perceptual models are ill-equipped for the high-dimensional data, such as text embeddings, central to modern psychology and AI. We introduce the double machine learning lens model, a framework that utilizes machine learning to handle such data. We applied this model to analyze how a modern AI and human perceivers judge social class from 9,513 aspirational essays written by 11-year-olds in 1969. A systematic comparison of 45 analytical approaches revealed that regularized linear models using dimensionality-reduced language embeddings significantly outperformed traditional dictionary-based methods and more complex non-linear models. Our top model accurately predicted human <mml:math xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mnf="http://cambridge.org/core/manifest" xmlns:cup="http://contentservices.cambridge.org" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://cambridge.org/core/metadata" xmlns:core="http://cambridge.org/core" xmlns:c="http://cambridge.org/core/content" display="inline"> <mml:mo stretchy="false">(</mml:mo> <mml:msubsup> <mml:mi>R</mml:mi> <mml:mrow> <mml:mi>C</mml:mi> <mml:mi>V</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msubsup> <mml:mo>=</mml:mo> <mml:mn>0.61</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:math> $(R^{2}_{CV} =0.61)$ left parenthesis upper R Subscript upper C upper V Superscript 2 Baseline equals 0.61 right parenthesis and AI <mml:math xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mnf="http://cambridge.org/core/manifest" xmlns:cup="http://contentservices.cambridge.org" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://cambridge.org/core/metadata" xmlns:core="http://cambridge.org/core" xmlns:c="http://cambridge.org/core/content" display="inline"> <mml:mo stretchy="false">(</mml:mo> <mml:msubsup> <mml:mi>R</mml:mi> <mml:mrow> <mml:mi>C</mml:mi> <mml:mi>V</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msubsup> <mml:mo>=</mml:mo> <mml:mn>0.56</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:math> $(R^{2}_{CV} =0.56)$ left parenthesis upper R Subscript upper C upper V Superscript 2 Baseline equals 0.56 right parenthesis social class perceptions, capturing over 85% of the total accuracy. These results suggest that “unmodeled knowledge” in perception may be an artifact of insufficient measurement tools rather than an unmeasurable intuitive process. We find that both AI and humans use many of the same textual cues (e.g., grammar, occupations, and cultural activities), only a subset of which are valid. Both appear to amplify subtle, real-world patterns into powerful, yet potentially discriminatory heuristics, where a small difference in actual social class creates a large difference in perception.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it