Bibliographic record
Abstract
This thesis uses novel data sets to examine the process of human capital accumulation at different points in the life cycle. Chapter 1 decomposes total childhood exposure effects – the causal effect of growing up in a better area – into separate school and neighborhood components. To do so, it brings together two research designs. First, I implement a spatial regression-discontinuity design based on institutional rules that assign different default schools to students of different linguistic backgrounds to estimate school effects. Second, I study students who move across neighborhoods in Montreal during childhood to estimate total exposure effects by exploiting variation in the timing of moves. I focus on measures of long-term educational attainment outcomes such as university enrollment. I find that total exposure effects are large, and that between 50 and 70% of the long-term benefits of moving to a better area are actually due to access to better schools rather than to the neighborhoods themselves. In chapter 2, joint with Graham Beattie and Philip Oreopoulos, we introduce a novel method for collecting a comprehensive set of non-academic characteristics to explore which measures best predict the wide variance in first-year college performance unaccounted for by past grades. Students whose first-year college average is far below expectations (divers) have a high propensity for procrastination and are considerably less conscientious than their peers. Divers are more likely to express superficial goals, hoping to 'get rich' quickly. In contrast, students who exceed expectations (thrivers) express more philanthropic goals, are purpose-driven, and are willing to study more hours per week to obtain the higher GPA they expect. Chapter 3 estimates the effect of linguistic enclaves on language skills. Using rich longitudinal data, I find that enclave size significantly impedes language acquisition, albeit the effect is smaller than cross-sectional models suggest. An unusually rich set of variables is used to generate bounds on the effect of enclaves and a complementary instrumental variable approach confirms the robustness of the results. Enclaves are unrelated to formal language course take-up rates, indicating that they affect language learning via social interactions among friends and colleagues rather than through formal education.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".