A Feminist Analysis of the Impact of COVID-19 on Olympic Female Athletes from Canada and the People’s Republic of China
Bibliographic record
Abstract
Gender equality in sports acquired unprecedented discussion in the past few decades with the efforts of sports organizations such as the United Nations (UN), athletes, professionals, and scholars worldwide. Girls’ participation, women’s media representation, participation of transgender athletes, equal opportunity, equal pay, etc. drew attention and awareness successfully. However, the outbreak of COVID-19 has been limiting the achievement of women in sports due to the cancellation of sports events, postponement of the Olympic Games, stay-at-home orders in lockdown, and restrictions on health measurements. This study utilized methods of semi-structured interviews, media analysis, and comparative analysis to examine the barriers faced by female Olympic athletes, along with their coping strategies during the COVID-19 pandemic. A comparison between Chinese and Canadian female athletes was implemented to gain insight into the barriers they struggled with in the pandemic, including the level of adaptation to COVID-19, length of building resilience to return to sports (RTS), accessibility of training facility, mental health problems, and other possible barriers. The results in this thesis indicated that Canadian female athletes had more significant obstacles in training whereas Chinese female counterparts did not perceive they had a considerable challenge since the pandemic. Despite the differences, the main similarity between Canadian and Chinese female athletes was the delay of the Olympic Games allowed them to re-concentrate on training and disassociate from the stressful ambience. The differences between the athletes’ experiences in the pandemic were due to different levels of accessibility to resources: female athletes in China acquired secured environments for training while Canadian female athletes had more opportunities to find various official resources online. Chinese athletes had better training accessibility and supervision from coaches while Canadian athletes had relatively limited access to training facilities. Overall, the pandemic increased gender inequality for elite female athletes to participate in sports and support systems. Sports organizations should comprehensively review and improve their operations to learn from the pandemic in order to support elite female athletes.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".