Editorial: Insights in sports social science
Bibliographic record
Abstract
Insights in sports social scienceWe are now entering the third decade of the 21st Century, and, especially in the last years, the achievements made by natural and social scientists have been exceptional, leading to major advancements in the fast-growing field of Sports and Active Living.This collection of articles is part of a series of Research Topics across the field of Sports and Active Living.This multi-disciplinary, editorial initiative is focused on new insights, novel developments, current challenges, latest discoveries, recent advances, and future perspectives in the field of sports social science.The goal of this special edition Research Topic was to shed light on the progress made in the past decade in the sport social science field, and on its future challenges to provide a thorough overview of the field.This article collection that has contributions from Canada, throughout Europe, UK, USA and South Africa will inspire, inform and provide direction and guidance to researchers in the field.This collection considers the findings from 11 research teams that from a variety of perspectives have identified current challenges in several sub-disciplines, and who have applied different methodologies to address those challenges.The different viewpoints are reflected in the types of articles that were included in the Research Topic, including articles containing original research, perspectives, a brief research report, a conceptual analysis, and a systematic review.What follows is a brief outline of the various projects.Testa conducted a study into extremism in the Bosnia and Herzegovina (BiH) football terraces, focusing on risk factors that govern the "entry" of BiH youth into extreme hardcore football fans groups and prolong their involvement in them.The study provided recommendations for BiH policymakers, security agencies, and football federations and clubs to understand and effectively respond to this threat for public security in BiH.Partly in response to the global Covid-19 pandemic, Weese et al. proposed transformative changes in what sport management academicians teach, how they teach, and where they teach, to facilitate working in flexible environments and across areas.Sport management professors are offered suggestions to help them seize the opportunities arising from the changing sports landscape and emerging entrepreneurial ventures.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.019 | 0.013 |
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".