And the award goes to…the Matthew Effect: Examining external status as a predictor of productivity and opportunity
Bibliographic record
Abstract
It has been suggested that increased status that comes from being an award winner can generate enduring advantages that compound over one's career via the Matthew Effect. However, research in this area has yielded conflicting results and has been unable to isolate the unique effect of status on career outcomes from the positive endogenous characteristics of award winners. In the current research, we attempt to address previous research limitations and examine if winning an award is associated with career outcomes (i.e., opportunities and productivity) irrespective of individual productivity levels prior to receiving an award. We examined our research questions using observational data of National Hockey League (NHL) league championship winners and non-winners (N = 427). By using a team award and several different analytic approaches we were able to examine the unique effects of affiliation-based external status, generated from an award win, on career outcomes. Our results generally show support for the Matthew Effect and suggest that affiliation-based external status, achieved by an award win, provides access to increased opportunities, which ultimately results in more productivity. We discuss the importance of incorporating opportunity and investigating its role in the cumulative advantage process and implications of the results.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".