Peer Effects and Social Networks in an MBA Program
Bibliographic record
Abstract
In this dissertation, I explore peer effects and social networks among MBA students in a Canadian business school. The unique feature of the data collected for this research is that the students are administratively assigned to small groups, providing plausibly exogenous variation that allows me to identify causal peer effects. Chapter 1, “Peer Effects in an MBA class”, establishes the existence and magnitude of the effects of peer characteristics on academic outcomes of MBA students. It shows that peer effects are heterogeneous across courses and student characteristics. Chapter 2, “Testing team allocation rules”, uses these results to find the allocations of students across peers that produce the highest grades for the Managerial Finance course. I find that separating students by their admission GPA (a proxy for academic ability) may result in the best grades in Managerial Finance class. I discuss the role of the business school and posit that academic achievement may not be the only outcome that is important for business school graduates. Finally, in Chapter 3, “Comparison of the Two Methods of Social Network Data Collection”, I compare two methods of social network data collection: a recollection and a recognition method. First, I present descriptive results of the data collected by these two methods. Then I use the approach described in Comola and Fafchamps (2017) to estimate the true proportion of links by using the information from the discordant answers. I conclude by commenting on the appropriate uses of the two methods of social network data collection.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".