Vocational Congruence for Esports and Gaming Video Content in Post-Secondary Education
Bibliographic record
Abstract
The video gaming industry has grown reliably year-over-year since its inception in the 1970s. In 2018, video game revenue worldwide peaked at 43.8 billion USD (Shieber, 2019), surpassing the film industry’s 41.1B (McClintock, 2019) by a healthy margin. As gaming’s relevance is cemented and its legitimacy becomes less of a debate, it continues to permeate other areas of the media, including competitive sports and traditional video content. Esports has seen an immense global market growth over the last five years, and as gaming video content continues to establish a strong foothold in streaming media, its influence on a wide range of careers is becoming more and more apparent—most noticeably in the television and film entertainment sector. This research examines and interrogates how the rise of esports and gaming video content (GVC) is poised to change the landscapes of both sports entertainment and streaming video, necessitating the need for integrative content production training, and a shift in emphasis from the traditional application of broadcast media in education. Through The Armoury project—which includes a framework for curriculum change and the introduction of GVC production facilities—it is the aim of this research to provide the necessary insights to inform vocational congruence within university course offerings to better serve students headed towards careers in the field of games and esports media.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".