Conceptualizing the Social Inclusion Potential of Esport to Support Future Sport for Development Agendas: A Capabilities Perspective
Bibliographic record
Abstract
Drawing on the capabilities approach to position esport within the Sport for Development (SfD) context, this paper highlights the social inclusion drivers of virtual engagement through esport as an innovative approach to progress current SfD methodologies. We present a new conceptual model in response to calls for enhanced theoretical understanding within SfD and specifically expose how esport can be used as a mechanism to support human development and inclusion outcomes. We focus specifically on gender equality as an exemplar of a prominent development objective; however, the model has applicability to any social inclusion related development aim. This paper proposes that esport should be welcomed as a new digital mechanism by policy makers, funders, and practitioners, as we indicate how this new conceptual model could be operationalized to aid SfD policy and practice.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".