Inclusion of Indigenous Peoples in Olympic legacy-shaping processes
Bibliographic record
Abstract
Scholars emphasise the need to understand how contested concepts, like social inclusion and legacy, are interpreted within specific contexts. However, there are a lack of critical studies on social legacies of sports mega-events. This study aims to analyse how social inclusion of marginalised groups is constructed in the legacy-shaping process of and bidding for the Olympic Games. Three cases were chosen in which the inclusion of Indigenous Peoples was stated as one of the goals of the bidding and organising committees: Sydney 2000, Vancouver 2010 and Tromsø 2014, 2018 bids. Translation theory and critical discourse analysis were used to understand how inclusion and legacy efforts are taken into action. The cases spread across space, time and bidding stage did not provide unique approaches to the formulations of legacies and inclusion. Despite the highlighted celebration of culture and diversity of communities in the documents, we interpret the inclusion discourse as a symbolic appreciation of Indigenous Peoples with attempts to address and solve the challenges connected to social exclusion. However, these attempts are characterised by postcolonial and assimilation thinking. A broader commitment is needed to create lasting social change through long-term initiatives created with and led by Indigenous Peoples.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".