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Record W4378981618 · doi:10.1287/mnsc.2023.4808

Work Style Diversity and Diffusion Within and Across Organizations: Evidence from Soviet-Style Hockey

2023· article· en· W4378981618 on OpenAlexaff
Francesco Amodio, Sam Hoey, Jeremy Schneider

Bibliographic record

VenueManagement Science · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCulture, Economy, and Development Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsLeagueStyle (visual arts)Diversity (politics)IndividualismPsychologyWork (physics)Demographic economicsPolitical scienceSociologyHistoryLawEconomicsEngineering

Abstract

fetched live from OpenAlex

Does the arrival of culturally diverse workers affect the work style of incumbent workers? We examine how the large influx of Russian hockey players in the National Hockey League after 1989 affected North American–born players. The Soviet style of hockey was largely based on skilled skating, constant movement, circling, and passing. In contrast, the North American play was more individualistic and linear, with higher emphasis on physical strength and aggressive behavior. Using 50 years of data at the player-game level, we show that (i) the number of penalty minutes per game increases steadily from 1970 to 1989, but decreases thereafter; (ii) although Russian players get systematically fewer penalty minutes in and after 1989, the trend reversal is driven by North American–born players; and (iii) the number of penalty minutes per game of North American–born players decreases systematically with the number of Russian players on their team and on the opposing team. Evidence shows that the hockey style brought about by Russian players was adopted and diffused within and across North American teams and players. This paper was accepted by Lamar Pierce, organizations. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2023.4808 .

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0070.001
Scholarly communication0.0000.001
Open science0.0000.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.293
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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