Catch and release? NHL expansion draft endowment effects
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate National Hockey League (NHL) expansion draft decisions to measure divestment aversion and endowment effects, and analyze bias and its affect on presumed rational analytic decision making. Design/methodology/approach A natural experiment with three variables (age, minutes played and presence of a prior relationship with a team’s management), filtered athletes that were exposed or protected to selection. A machine learning algorithm trained on a group of 17 teams was applied to the remaining 13 teams. Findings Athletes with pre-existing management relationships were 1.7 times more likely to be protected. Athletes playing fewer relative position minutes were less likely to be protected, as were older athletes. Athlete selection was predominantly determined by time on ice. Research limitations/implications This represents a single set of independent decisions using publicly available data absent of context. The results may not be generalizable beyond the NHL or sport. Practical implications The research confirms the affect of prior relationships on decision making and provides further evidence of measurable sub-optimal decision making. Social implications Decision making has implications throughout human resources and impacts competitiveness and productivity. This adds to the need for managers to recognize and implement de-biasing in areas such as hiring, performance appraisal and downsizing. Originality/value This natural experiment involving high-stakes decision makers confirms bias in a setting that has been dominated by students, low stakes or artificial settings.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".